


"""simple docstring"""



import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class _lowerCAmelCase    (				snake_case_     ):
    __UpperCAmelCase			:	torch.FloatTensor
    __UpperCAmelCase			:	Optional[torch.FloatTensor]							  =  None







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Dict=0.999						,							lowercase  :  Dict="cosine"						,							)     ->						Optional[Any]:


    """simple docstring"""





    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowercase  :  Union[str, Any]			):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2			) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(lowercase  :  Tuple			):
            return math.exp(t * -12.0			)

    else:
        raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'			)

    snake_case   :				List[str]				 =   []
    for i in range(lowercase			):
        snake_case   :				int				 =   i / num_diffusion_timesteps
        snake_case   :				str				 =   (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(lowercase			) / alpha_bar_fn(lowercase			)						,							lowercase			)			)
    return torch.tensor(lowercase						,							dtype=torch.floataa			)







class _lowerCAmelCase    (				snake_case_   ,							snake_case_     ):
    __UpperCAmelCase			:	Optional[int]							  =  1







    @register_to_config
    def __init__(     self						,     UpperCamelCase__ = 1000						,     UpperCamelCase__ = 0.0001						,     UpperCamelCase__ = 0.02						,     UpperCamelCase__ = "linear"						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     UpperCamelCase__ = True						,     UpperCamelCase__ = 0						,     UpperCamelCase__ = "epsilon"						,     UpperCamelCase__ = 1.0						,     **UpperCamelCase__						,     )     ->		str:

        '''simple docstring'''




        if kwargs.get("set_alpha_to_one"						,     UpperCamelCase__						) is not None:
            snake_case   :				str				 =   (
                "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
            )
            deprecate("set_alpha_to_one"						,     "1.0.0"						,     UpperCamelCase__						,     standard_warn=UpperCamelCase__						)
            snake_case   :				Optional[int]				 =   kwargs["set_alpha_to_one"]
        if trained_betas is not None:
            snake_case   :				List[Any]				 =   torch.tensor(UpperCamelCase__						,     dtype=torch.floataa						)
        elif beta_schedule == "linear":
            snake_case   :				Union[str, Any]				 =   torch.linspace(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     dtype=torch.floataa						)
        elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            snake_case   :				str				 =   (
                torch.linspace(beta_start**0.5						,     beta_end**0.5						,     UpperCamelCase__						,     dtype=torch.floataa						) ** 2
            )
        elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            snake_case   :				Any				 =   betas_for_alpha_bar(UpperCamelCase__						)
        else:
            raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'						)

        snake_case   :				Optional[Any]				 =   1.0 - self.betas
        snake_case   :				Union[str, Any]				 =   torch.cumprod(self.alphas						,     dim=0						)

        # At every step in inverted ddim, we are looking into the next alphas_cumprod
        # For the final step, there is no next alphas_cumprod, and the index is out of bounds
        # `set_alpha_to_zero` decides whether we set this parameter simply to zero
        # in this case, self.step() just output the predicted noise
        # or whether we use the final alpha of the "non-previous" one.
        snake_case   :				List[str]				 =   torch.tensor(0.0						) if set_alpha_to_zero else self.alphas_cumprod[-1]

        # standard deviation of the initial noise distribution
        snake_case   :				Optional[Any]				 =   1.0

        # setable values
        snake_case   :				Optional[Any]				 =   None
        snake_case   :				Optional[int]				 =   torch.from_numpy(np.arange(0						,     UpperCamelCase__						).copy().astype(np.intaa						)						)







    def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		torch.FloatTensor:

        '''simple docstring'''




        return sample







    def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		str:

        '''simple docstring'''




        if num_inference_steps > self.config.num_train_timesteps:
            raise ValueError(
                F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                F' maximal {self.config.num_train_timesteps} timesteps.'						)

        snake_case   :				int				 =   num_inference_steps
        snake_case   :				Union[str, Any]				 =   self.config.num_train_timesteps // self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        snake_case   :				List[str]				 =   (np.arange(0						,     UpperCamelCase__						) * step_ratio).round().copy().astype(np.intaa						)
        snake_case   :				Tuple				 =   torch.from_numpy(UpperCamelCase__						).to(UpperCamelCase__						)
        self.timesteps += self.config.steps_offset







    def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = 0.0						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     )     ->		Union[DDIMSchedulerOutput, Tuple]:

        '''simple docstring'''




        snake_case   :				Dict				 =   timestep + self.config.num_train_timesteps // self.num_inference_steps

        # 2. compute alphas, betas
        # change original implementation to exactly match noise levels for analogous forward process
        snake_case   :				Any				 =   self.alphas_cumprod[timestep]
        snake_case   :				Dict				 =   (
            self.alphas_cumprod[prev_timestep]
            if prev_timestep < self.config.num_train_timesteps
            else self.final_alpha_cumprod
        )

        snake_case   :				str				 =   1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
            snake_case   :				List[str]				 =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            snake_case   :				Union[str, Any]				 =   model_output
        elif self.config.prediction_type == "sample":
            snake_case   :				str				 =   model_output
            snake_case   :				Optional[int]				 =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
            snake_case   :				Any				 =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
            snake_case   :				Optional[int]				 =   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
            raise ValueError(
                F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
                " `v_prediction`"						)

        # 4. Clip or threshold "predicted x_0"
        if self.config.clip_sample:
            snake_case   :				int				 =   pred_original_sample.clamp(
                -self.config.clip_sample_range						,     self.config.clip_sample_range						)

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        snake_case   :				List[Any]				 =   (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        snake_case   :				Dict				 =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
            return (prev_sample, pred_original_sample)
        return DDIMSchedulerOutput(prev_sample=UpperCamelCase__						,     pred_original_sample=UpperCamelCase__						)







    def __len__(     self						)     ->		Any:

        '''simple docstring'''




        return self.config.num_train_timesteps









"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int]=False			)     ->						Union[str, Any]:


    """simple docstring"""





    snake_case   :				Dict				 =   OmegaConf.load(lowercase			)
    if display:
        print(yaml.dump(OmegaConf.to_container(lowercase			)			)			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Dict=None						,							lowercase  :  Dict=None			)     ->						Union[str, Any]:


    """simple docstring"""





    if conf_path is None:
        snake_case   :				Optional[Any]				 =   "./model_checkpoints/vqgan_only.yaml"
    snake_case   :				Union[str, Any]				 =   load_config(lowercase						,							display=lowercase			)
    snake_case   :				List[Any]				 =   VQModel(**config.model.params			)
    if ckpt_path is None:
        snake_case   :				Optional[int]				 =   "./model_checkpoints/vqgan_only.pt"
    snake_case   :				Union[str, Any]				 =   torch.load(lowercase						,							map_location=lowercase			)
    if ".ckpt" in ckpt_path:
        snake_case   :				Union[str, Any]				 =   sd["state_dict"]
    model.load_state_dict(lowercase						,							strict=lowercase			)
    model.to(lowercase			)
    del sd
    return model







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  List[str]			)     ->						List[str]:


    """simple docstring"""





    snake_case ,snake_case ,snake_case   :				List[Any]				 =   model.encode(lowercase			)
    print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'			)
    snake_case   :				Union[str, Any]				 =   model.decode(lowercase			)
    return xrec







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  str=False			)     ->						Optional[int]:


    """simple docstring"""





    snake_case ,snake_case   :				Any				 =   string.rsplit("."						,							1			)
    if reload:
        snake_case   :				List[Any]				 =   importlib.import_module(lowercase			)
        importlib.reload(lowercase			)
    return getattr(importlib.import_module(lowercase						,							package=lowercase			)						,							cls			)







def   __lowerCAmelCase						(						lowercase  :  List[str]			)     ->						Union[str, Any]:


    """simple docstring"""





    if "target" not in config:
        raise KeyError("Expected key `target` to instantiate."			)
    return get_obj_from_str(config["target"]			)(**config.get("params"						,							{}			)			)







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  List[str]						,							lowercase  :  Tuple=True						,							lowercase  :  Optional[Any]=True			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   instantiate_from_config(lowercase			)
    if sd is not None:
        model.load_state_dict(lowercase			)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Union[str, Any]			)     ->						Union[str, Any]:


    """simple docstring"""





    if ckpt:
        snake_case   :				Dict				 =   torch.load(lowercase						,							map_location="cpu"			)
        snake_case   :				Any				 =   pl_sd["global_step"]
        print(F'loaded model from global step {global_step}.'			)
    else:
        snake_case   :				Any				 =   {"state_dict": None}
        snake_case   :				List[str]				 =   None
    snake_case   :				Dict				 =   load_model_from_config(config.model						,							pl_sd["state_dict"]						,							gpu=lowercase						,							eval_mode=lowercase			)["model"]
    return model, global_step





