





'''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							(				_lowerCamelCase   :   List[Any]		,			_lowerCamelCase   :   int			):
    A__							=     checkpoint

    A__							=     {}

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

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

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

    # Retrieves the keys for the encoder down blocks only
    A__							=     len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "encoder.down" in layer}			)
    A__							=     {
        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
    A__							=     len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "decoder.up" in layer}			)
    A__							=     {
        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			):
        A__							=     [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:
            A__							=     vae_state_dict.pop(
                F"encoder.down.{i}.downsample.conv.weight"			)
            A__							=     vae_state_dict.pop(
                F"encoder.down.{i}.downsample.conv.bias"			)

        A__							=     renew_vae_resnet_paths(_lowerCamelCase			)
        A__							=     {"old": F"down.{i}.block", "new": F"down_blocks.{i}.resnets"}
        assign_to_checkpoint(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase		,			additional_replacements=[meta_path]		,			config=_lowerCamelCase			)

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

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

    A__							=     [key for key in vae_state_dict if "encoder.mid.attn" in key]
    A__							=     renew_vae_attention_paths(_lowerCamelCase			)
    A__							=     {"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			):
        A__							=     num_up_blocks - 1 - i
        A__							=     [
            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:
            A__							=     vae_state_dict[
                F"decoder.up.{block_id}.upsample.conv.weight"
            ]
            A__							=     vae_state_dict[
                F"decoder.up.{block_id}.upsample.conv.bias"
            ]

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

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

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

    A__							=     [key for key in vae_state_dict if "decoder.mid.attn" in key]
    A__							=     renew_vae_attention_paths(_lowerCamelCase			)
    A__							=     {"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 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str		,			):
    # Only support V1
    A__							=     requests.get(
        " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"			)
    A__							=     io.BytesIO(r.content			)

    A__							=     OmegaConf.load(_lowerCamelCase			)
    A__							=     5_12
    A__							=     "cuda" if torch.cuda.is_available() else "cpu"
    if checkpoint_path.endswith("safetensors"			):
        from safetensors import safe_open

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

    # Convert the VAE model.
    A__							=     create_vae_diffusers_config(_lowerCamelCase		,			image_size=_lowerCamelCase			)
    A__							=     custom_convert_ldm_vae_checkpoint(_lowerCamelCase		,			_lowerCamelCase			)

    A__							=     AutoencoderKL(**_lowerCamelCase			)
    vae.load_state_dict(_lowerCamelCase			)
    vae.save_pretrained(_lowerCamelCase			)


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

  __lowerCAmelCase				:      List[str]						      =parser.parse_args()

  vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)










'''simple docstring'''

__lowerCAmelCase				:      List[str]						      ="Alexander Joslin"

import operator as op

from .stack import Stack





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    A__							=     Stack()
    A__							=     Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(_lowerCamelCase			)			)
        elif i in operators:
            # RULE 2
            operator_stack.push(_lowerCamelCase			)
        elif i == ")":
            # RULE 4
            A__							=     operator_stack.peek()
            operator_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()

            A__							=     operators[opr](_lowerCamelCase		,			_lowerCamelCase			)
            operand_stack.push(_lowerCamelCase			)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  __lowerCAmelCase				:      Dict						      ="(5 + ((4 * 2) * (2 + 3)))"
  # answer = 45
  print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



