




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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_UpperCAmelCase						:    Optional[int]      =  logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class  __lowerCAmelCase							(				lowerCAmelCase):
       _a			       =    42
       _a			       =    None







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase=0.999,      lowerCamelCase="cosine",      ):
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowerCamelCase ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

  elif alpha_transform_type == "exp":

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

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

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







class  __lowerCAmelCase							(				lowerCAmelCase   ,			lowerCAmelCase):
       _a			       =    [e.name for e in KarrasDiffusionSchedulers]
       _a			       =    1






       @register_to_config
       def __init__( self:       Optional[Any]							,       _lowerCAmelCase:       int = 10_00							,       _lowerCAmelCase:       float = 0.00_01							,       _lowerCAmelCase:       float = 0.02							,       _lowerCAmelCase:       str = "linear"							,       _lowerCAmelCase:       Optional[Union[np.ndarray, List[float]]] = None							,       _lowerCAmelCase:       str = "epsilon"							,       _lowerCAmelCase:       str = "linear"							,       _lowerCAmelCase:       Optional[bool] = False							,       _lowerCAmelCase:       str = "linspace"							,       _lowerCAmelCase:       int = 0							,       ):
         if trained_betas is not None:
           lowercase     :Any   						=    torch.tensor(_lowerCAmelCase							,       dtype=torch.floataa					)
         elif beta_schedule == "linear":
           lowercase     :Tuple   						=    torch.linspace(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       dtype=torch.floataa					)
         elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           lowercase     :Tuple   						=    (
               torch.linspace(beta_start**0.5							,       beta_end**0.5							,       _lowerCAmelCase							,       dtype=torch.floataa					) ** 2
           )
         elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           lowercase     :str   						=    betas_for_alpha_bar(_lowerCAmelCase					)
         else:
           raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"					)

         lowercase     :Optional[Any]   						=    1.0 - self.betas
         lowercase     :Tuple   						=    torch.cumprod(self.alphas							,       dim=0					)

         lowercase     :Optional[Any]   						=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
         lowercase     :List[Any]   						=    np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
         lowercase     :Any   						=    torch.from_numpy(_lowerCAmelCase					)

         # setable values
         lowercase     :List[str]   						=    None
         lowercase     :Any   						=    np.linspace(0							,       num_train_timesteps - 1							,       _lowerCAmelCase							,       dtype=_lowerCAmelCase					)[::-1].copy()
         lowercase     :Any   						=    torch.from_numpy(_lowerCAmelCase					)
         lowercase     :Union[str, Any]   						=    False
         lowercase     :Any   						=    use_karras_sigmas






       @property
       def        SCREAMING_SNAKE_CASE     ( self:       Tuple					):
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       Union[float, torch.FloatTensor]					):
         if isinstance(_lowerCAmelCase							,       torch.Tensor					):
           lowercase     :Any   						=    timestep.to(self.timesteps.device					)
         lowercase     :Tuple   						=    (self.timesteps == timestep).nonzero().item()
         lowercase     :List[Any]   						=    self.sigmas[step_index]

         lowercase     :Union[str, Any]   						=    sample / ((sigma**2 + 1) ** 0.5)

         lowercase     :int   						=    True
         return sample






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       int							,       _lowerCAmelCase:       Union[str, torch.device] = None					):
         lowercase     :Any   						=    num_inference_steps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
           lowercase     :Tuple   						=    np.linspace(0							,       self.config.num_train_timesteps - 1							,       _lowerCAmelCase							,       dtype=_lowerCAmelCase					)[
               ::-1
           ].copy()
         elif self.config.timestep_spacing == "leading":
           lowercase     :Tuple   						=    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
           lowercase     :Tuple   						=    (np.arange(0							,       _lowerCAmelCase					) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase					)
           timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
           lowercase     :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
           lowercase     :Dict   						=    (np.arange(self.config.num_train_timesteps							,       0							,       -step_ratio					)).round().copy().astype(_lowerCAmelCase					)
           timesteps -= 1
         else:
           raise ValueError(
               F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."					)

         lowercase     :List[Any]   						=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
         lowercase     :Dict   						=    np.log(_lowerCAmelCase					)

         if self.config.interpolation_type == "linear":
           lowercase     :Any   						=    np.interp(_lowerCAmelCase							,       np.arange(0							,       len(_lowerCAmelCase					)					)							,       _lowerCAmelCase					)
         elif self.config.interpolation_type == "log_linear":
           lowercase     :List[Any]   						=    torch.linspace(np.log(sigmas[-1]					)							,       np.log(sigmas[0]					)							,       num_inference_steps + 1					).exp()
         else:
           raise ValueError(
               F"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
               " 'linear' or 'log_linear'"					)

         if self.use_karras_sigmas:
           lowercase     :List[str]   						=    self._convert_to_karras(in_sigmas=_lowerCAmelCase							,       num_inference_steps=self.num_inference_steps					)
           lowercase     :int   						=    np.array([self._sigma_to_t(_lowerCAmelCase							,       _lowerCAmelCase					) for sigma in sigmas]					)

         lowercase     :Optional[Any]   						=    np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
         lowercase     :Union[str, Any]   						=    torch.from_numpy(_lowerCAmelCase					).to(device=_lowerCAmelCase					)
         if str(_lowerCAmelCase					).startswith("mps"					):
           # mps does not support float64
           lowercase     :Optional[Any]   						=    torch.from_numpy(_lowerCAmelCase					).to(_lowerCAmelCase							,       dtype=torch.floataa					)
         else:
           lowercase     :Dict   						=    torch.from_numpy(_lowerCAmelCase					).to(device=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Any							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       Optional[Any]					):
         # get log sigma
         lowercase     :Dict   						=    np.log(_lowerCAmelCase					)

         # get distribution
         lowercase     :Optional[Any]   						=    log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         lowercase     :Any   						=    np.cumsum((dists >= 0)							,       axis=0					).argmax(axis=0					).clip(max=log_sigmas.shape[0] - 2					)
         lowercase     :List[Any]   						=    low_idx + 1

         lowercase     :Dict   						=    log_sigmas[low_idx]
         lowercase     :int   						=    log_sigmas[high_idx]

         # interpolate sigmas
         lowercase     :Optional[Any]   						=    (low - log_sigma) / (low - high)
         lowercase     :Tuple   						=    np.clip(_lowerCAmelCase							,       0							,       1					)

         # transform interpolation to time range
         lowercase     :List[Any]   						=    (1 - w) * low_idx + w * high_idx
         lowercase     :Dict   						=    t.reshape(sigma.shape					)
         return t






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       Optional[Any]					):
         lowercase     :float   						=    in_sigmas[-1].item()
         lowercase     :float   						=    in_sigmas[0].item()

         lowercase     :int   						=    7.0  # 7.0 is the value used in the paper
         lowercase     :Tuple   						=    np.linspace(0							,       1							,       _lowerCAmelCase					)
         lowercase     :Union[str, Any]   						=    sigma_min ** (1 / rho)
         lowercase     :List[Any]   						=    sigma_max ** (1 / rho)
         lowercase     :int   						=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       Union[float, torch.FloatTensor]							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       float = 0.0							,       _lowerCAmelCase:       float = 0.0							,       _lowerCAmelCase:       float = float("inf"					)							,       _lowerCAmelCase:       float = 1.0							,       _lowerCAmelCase:       Optional[torch.Generator] = None							,       _lowerCAmelCase:       bool = True							,       ):

         if (
             isinstance(_lowerCAmelCase							,       _lowerCAmelCase					)
             or isinstance(_lowerCAmelCase							,       torch.IntTensor					)
             or isinstance(_lowerCAmelCase							,       torch.LongTensor					)
         ):
           raise ValueError(
               (
                   "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                   " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                   " one of the `scheduler.timesteps` as a timestep."
               )							,       )

         if not self.is_scale_input_called:
           logger.warning(
               "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
               "See `StableDiffusionPipeline` for a usage example."					)

         if isinstance(_lowerCAmelCase							,       torch.Tensor					):
           lowercase     :Union[str, Any]   						=    timestep.to(self.timesteps.device					)

         lowercase     :Optional[Any]   						=    (self.timesteps == timestep).nonzero().item()
         lowercase     :Optional[Any]   						=    self.sigmas[step_index]

         lowercase     :Any   						=    min(s_churn / (len(self.sigmas					) - 1)							,       2**0.5 - 1					) if s_tmin <= sigma <= s_tmax else 0.0

         lowercase     :List[str]   						=    randn_tensor(
             model_output.shape							,       dtype=model_output.dtype							,       device=model_output.device							,       generator=_lowerCAmelCase					)

         lowercase     :Any   						=    noise * s_noise
         lowercase     :List[str]   						=    sigma * (gamma + 1)

         if gamma > 0:
           lowercase     :int   						=    sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         # NOTE: "original_sample" should not be an expected prediction_type but is left in for
         # backwards compatibility
         if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
           lowercase     :str   						=    model_output
         elif self.config.prediction_type == "epsilon":
           lowercase     :Optional[int]   						=    sample - sigma_hat * model_output
         elif self.config.prediction_type == "v_prediction":
           # * c_out + input * c_skip
           lowercase     :List[Any]   						=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
           raise ValueError(
               F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"					)

         # 2. Convert to an ODE derivative
         lowercase     :Dict   						=    (sample - pred_original_sample) / sigma_hat

         lowercase     :List[str]   						=    self.sigmas[step_index + 1] - sigma_hat

         lowercase     :Any   						=    sample + derivative * dt

         if not return_dict:
           return (prev_sample,)

         return EulerDiscreteSchedulerOutput(prev_sample=_lowerCAmelCase							,       pred_original_sample=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       Dict							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       torch.FloatTensor							,       _lowerCAmelCase:       torch.FloatTensor							,       ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         lowercase     :Tuple   						=    self.sigmas.to(device=original_samples.device							,       dtype=original_samples.dtype					)
         if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase					):
           # mps does not support float64
           lowercase     :int   						=    self.timesteps.to(original_samples.device							,       dtype=torch.floataa					)
           lowercase     :int   						=    timesteps.to(original_samples.device							,       dtype=torch.floataa					)
         else:
           lowercase     :Any   						=    self.timesteps.to(original_samples.device					)
           lowercase     :List[str]   						=    timesteps.to(original_samples.device					)

         lowercase     :int   						=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         lowercase     :Union[str, Any]   						=    sigmas[step_indices].flatten()
         while len(sigma.shape					) < len(original_samples.shape					):
           lowercase     :List[str]   						=    sigma.unsqueeze(-1					)

         lowercase     :List[Any]   						=    original_samples + noise * sigma
         return noisy_samples






       def __len__( self:       Dict					):
         return self.config.num_train_timesteps









import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json



def    UpperCAmelCase__	(  lowerCamelCase ):
  lowercase     :Optional[Any]   						=    filter(lambda lowerCamelCase : p.requires_grad,      model.parameters() )
  lowercase     :Optional[int]   						=    sum([np.prod(p.size() ) for p in model_parameters] )
  return params


_UpperCAmelCase						:    Optional[Any]      =  logging.getLogger(__name__)




class  __lowerCAmelCase							(				pl.Callback):






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       List[Any]							,       _lowerCAmelCase:       Union[str, Any]					):
         lowercase     :Dict   						=    {F"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
         pl_module.logger.log_metrics(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       List[str]=True					):
         logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"					)
         lowercase     :Tuple   						=    trainer.callback_metrics
         trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}					)
         # Log results
         lowercase     :Optional[int]   						=    Path(pl_module.hparams.output_dir					)
         if type_path == "test":
           lowercase     :List[Any]   						=    od / "test_results.txt"
           lowercase     :Optional[int]   						=    od / "test_generations.txt"
         else:
           # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
           # If people want this it will be easy enough to add back.
           lowercase     :int   						=    od / F"{type_path}_results/{trainer.global_step:05d}.txt"
           lowercase     :Tuple   						=    od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
           results_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
           generations_file.parent.mkdir(exist_ok=_lowerCAmelCase					)
         with open(_lowerCAmelCase							,       "a+"					) as writer:
           for key in sorted(_lowerCAmelCase					):
             if key in ["log", "progress_bar", "preds"]:
               continue
             lowercase     :int   						=    metrics[key]
             if isinstance(_lowerCAmelCase							,       torch.Tensor					):
               lowercase     :int   						=    val.item()
             lowercase     :Optional[Any]   						=    F"{key}: {val:.6f}\n"
             writer.write(_lowerCAmelCase					)

         if not save_generations:
           return

         if "preds" in metrics:
           lowercase     :str   						=    "\n".join(metrics["preds"]					)
           generations_file.open("w+"					).write(_lowerCAmelCase					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]							,       _lowerCAmelCase:       Tuple							,       _lowerCAmelCase:       Tuple					):
         try:
           lowercase     :Optional[int]   						=    pl_module.model.model.num_parameters()
         except AttributeError:
           lowercase     :Dict   						=    pl_module.model.num_parameters()

         lowercase     :Any   						=    count_trainable_parameters(_lowerCAmelCase					)
         # mp stands for million parameters
         trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       pl.LightningModule					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         return self._write_logs(_lowerCAmelCase							,       _lowerCAmelCase							,       "test"					)






       @rank_zero_only
       def        SCREAMING_SNAKE_CASE     ( self:       List[str]							,       _lowerCAmelCase:       pl.Trainer							,       _lowerCAmelCase:       Dict					):
         save_json(pl_module.metrics							,       pl_module.metrics_save_path					)
         # Uncommenting this will save val generations
         # return self._write_logs(trainer, pl_module, "valid")







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=1,      lowerCamelCase=False ):
  if metric == "rouge2":
    lowercase     :str   						=    "{val_avg_rouge2:.4f}-{step_count}"
  elif metric == "bleu":
    lowercase     :int   						=    "{val_avg_bleu:.4f}-{step_count}"
  elif metric == "loss":
    lowercase     :List[str]   						=    "{val_avg_loss:.4f}-{step_count}"
  else:
    raise NotImplementedError(
        F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
        " this function." )

  lowercase     :int   						=    ModelCheckpoint(
      dirpath=lowerCamelCase,      filename=lowerCamelCase,      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      save_top_k=lowerCamelCase,      )
  return checkpoint_callback







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase ):
  return EarlyStopping(
      monitor=F"val_{metric}",      mode="min" if "loss" in metric else "max",      patience=lowerCamelCase,      verbose=lowerCamelCase,      )



