



"""simple docstring"""


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


snake_case_         = logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						__UpperCamelCase	       =							42
						__UpperCamelCase	       =							None


def 				_lowerCAmelCase   (     lowercase_	,			lowercase_=0.9_9_9	,			lowercase_="cosine"	,			):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowercase_		):
																		return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(lowercase_		):
																		return math.exp(t * -1_2.0		)

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

						UpperCAmelCase    =				[]
						for i in range(lowercase_		):
												UpperCAmelCase    =				i / num_diffusion_timesteps
												UpperCAmelCase    =				(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    A_    (  SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						__UpperCamelCase	       =							[e.name for e in KarrasDiffusionSchedulers]
						__UpperCamelCase	       =							1






						@register_to_config
						def __init__(      self				:Any     ,						lowercase_				:int = 10_00     ,						lowercase_				:float = 0.0001     ,						lowercase_				:float = 0.02     ,						lowercase_				:str = "linear"     ,						lowercase_				:Optional[Union[np.ndarray, List[float]]] = None     ,						lowercase_				:str = "epsilon"     ,						lowercase_				:str = "linear"     ,						lowercase_				:Optional[bool] = False     ,						lowercase_				:str = "linspace"     ,						lowercase_				:int = 0     ,						)       ->    Dict:
												if trained_betas is not None:
																		UpperCAmelCase    =				torch.tensor(lowercase_     ,						dtype=torch.floataa      )
												elif beta_schedule == "linear":
																		UpperCAmelCase    =				torch.linspace(lowercase_     ,						lowercase_     ,						lowercase_     ,						dtype=torch.floataa      )
												elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		UpperCAmelCase    =				(
																		    torch.linspace(beta_start**0.5     ,						beta_end**0.5     ,						lowercase_     ,						dtype=torch.floataa      ) ** 2
																		)
												elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		UpperCAmelCase    =				betas_for_alpha_bar(lowercase_      )
												else:
																		raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""      )

												UpperCAmelCase    =				1.0 - self.betas
												UpperCAmelCase    =				torch.cumprod(self.alphas     ,						dim=0      )

												UpperCAmelCase    =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
												UpperCAmelCase    =				np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
												UpperCAmelCase    =				torch.from_numpy(lowercase_      )

												# setable values
												UpperCAmelCase    =				None
												UpperCAmelCase    =				np.linspace(0     ,						num_train_timesteps - 1     ,						lowercase_     ,						dtype=lowercase_      )[::-1].copy()
												UpperCAmelCase    =				torch.from_numpy(lowercase_      )
												UpperCAmelCase    =				False
												UpperCAmelCase    =				use_karras_sigmas






						@property
						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    Any:
												# 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 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:torch.FloatTensor     ,						lowercase_				:Union[float, torch.FloatTensor]      )       ->    torch.FloatTensor:
												if isinstance(lowercase_     ,						torch.Tensor      ):
																		UpperCAmelCase    =				timestep.to(self.timesteps.device      )
												UpperCAmelCase    =				(self.timesteps == timestep).nonzero().item()
												UpperCAmelCase    =				self.sigmas[step_index]

												UpperCAmelCase    =				sample / ((sigma**2 + 1) ** 0.5)

												UpperCAmelCase    =				True
												return sample






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:int     ,						lowercase_				:Union[str, torch.device] = None      )       ->    Union[str, Any]:
												UpperCAmelCase    =				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":
																		UpperCAmelCase    =				np.linspace(0     ,						self.config.num_train_timesteps - 1     ,						lowercase_     ,						dtype=lowercase_      )[
																		    ::-1
																		].copy()
												elif self.config.timestep_spacing == "leading":
																		UpperCAmelCase    =				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
																		UpperCAmelCase    =				(np.arange(0     ,						lowercase_      ) * step_ratio).round()[::-1].copy().astype(lowercase_      )
																		timesteps += self.config.steps_offset
												elif self.config.timestep_spacing == "trailing":
																		UpperCAmelCase    =				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
																		UpperCAmelCase    =				(np.arange(self.config.num_train_timesteps     ,						0     ,						-step_ratio      )).round().copy().astype(lowercase_      )
																		timesteps -= 1
												else:
																		raise ValueError(
																		    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""      )

												UpperCAmelCase    =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
												UpperCAmelCase    =				np.log(lowercase_      )

												if self.config.interpolation_type == "linear":
																		UpperCAmelCase    =				np.interp(lowercase_     ,						np.arange(0     ,						len(lowercase_      )      )     ,						lowercase_      )
												elif self.config.interpolation_type == "log_linear":
																		UpperCAmelCase    =				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:
																		UpperCAmelCase    =				self._convert_to_karras(in_sigmas=lowercase_     ,						num_inference_steps=self.num_inference_steps      )
																		UpperCAmelCase    =				np.array([self._sigma_to_t(lowercase_     ,						lowercase_      ) for sigma in sigmas]      )

												UpperCAmelCase    =				np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
												UpperCAmelCase    =				torch.from_numpy(lowercase_      ).to(device=lowercase_      )
												if str(lowercase_      ).startswith('mps'      ):
																		# mps does not support float64
																		UpperCAmelCase    =				torch.from_numpy(lowercase_      ).to(lowercase_     ,						dtype=torch.floataa      )
												else:
																		UpperCAmelCase    =				torch.from_numpy(lowercase_      ).to(device=lowercase_      )






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						lowercase_				:Optional[Any]     ,						lowercase_				:Any      )       ->    List[Any]:
												# get log sigma
												UpperCAmelCase    =				np.log(lowercase_      )

												# get distribution
												UpperCAmelCase    =				log_sigma - log_sigmas[:, np.newaxis]

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

												UpperCAmelCase    =				log_sigmas[low_idx]
												UpperCAmelCase    =				log_sigmas[high_idx]

												# interpolate sigmas
												UpperCAmelCase    =				(low - log_sigma) / (low - high)
												UpperCAmelCase    =				np.clip(lowercase_     ,						0     ,						1      )

												# transform interpolation to time range
												UpperCAmelCase    =				(1 - w) * low_idx + w * high_idx
												UpperCAmelCase    =				t.reshape(sigma.shape      )
												return t






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						lowercase_				:torch.FloatTensor     ,						lowercase_				:Any      )       ->    torch.FloatTensor:
												UpperCAmelCase    =				in_sigmas[-1].item()
												UpperCAmelCase    =				in_sigmas[0].item()

												UpperCAmelCase    =				7.0  # 7.0 is the value used in the paper
												UpperCAmelCase    =				np.linspace(0     ,						1     ,						lowercase_      )
												UpperCAmelCase    =				sigma_min ** (1 / rho)
												UpperCAmelCase    =				sigma_max ** (1 / rho)
												UpperCAmelCase    =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.FloatTensor     ,						lowercase_				:Union[float, torch.FloatTensor]     ,						lowercase_				:torch.FloatTensor     ,						lowercase_				:float = 0.0     ,						lowercase_				:float = 0.0     ,						lowercase_				:float = float('inf'      )     ,						lowercase_				:float = 1.0     ,						lowercase_				:Optional[torch.Generator] = None     ,						lowercase_				:bool = True     ,						)       ->    Union[EulerDiscreteSchedulerOutput, Tuple]:

												if (
												    isinstance(lowercase_     ,						lowercase_      )
												    or isinstance(lowercase_     ,						torch.IntTensor      )
												    or isinstance(lowercase_     ,						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(lowercase_     ,						torch.Tensor      ):
																		UpperCAmelCase    =				timestep.to(self.timesteps.device      )

												UpperCAmelCase    =				(self.timesteps == timestep).nonzero().item()
												UpperCAmelCase    =				self.sigmas[step_index]

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

												UpperCAmelCase    =				randn_tensor(
												    model_output.shape     ,						dtype=model_output.dtype     ,						device=model_output.device     ,						generator=lowercase_      )

												UpperCAmelCase    =				noise * s_noise
												UpperCAmelCase    =				sigma * (gamma + 1)

												if gamma > 0:
																		UpperCAmelCase    =				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":
																		UpperCAmelCase    =				model_output
												elif self.config.prediction_type == "epsilon":
																		UpperCAmelCase    =				sample - sigma_hat * model_output
												elif self.config.prediction_type == "v_prediction":
																		# * c_out + input * c_skip
																		UpperCAmelCase    =				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
												UpperCAmelCase    =				(sample - pred_original_sample) / sigma_hat

												UpperCAmelCase    =				self.sigmas[step_index + 1] - sigma_hat

												UpperCAmelCase    =				sample + derivative * dt

												if not return_dict:
																		return (prev_sample,)

												return EulerDiscreteSchedulerOutput(prev_sample=lowercase_     ,						pred_original_sample=lowercase_      )






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:torch.FloatTensor     ,						lowercase_				:torch.FloatTensor     ,						lowercase_				:torch.FloatTensor     ,						)       ->    torch.FloatTensor:
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												UpperCAmelCase    =				self.sigmas.to(device=original_samples.device     ,						dtype=original_samples.dtype      )
												if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_      ):
																		# mps does not support float64
																		UpperCAmelCase    =				self.timesteps.to(original_samples.device     ,						dtype=torch.floataa      )
																		UpperCAmelCase    =				timesteps.to(original_samples.device     ,						dtype=torch.floataa      )
												else:
																		UpperCAmelCase    =				self.timesteps.to(original_samples.device      )
																		UpperCAmelCase    =				timesteps.to(original_samples.device      )

												UpperCAmelCase    =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

												UpperCAmelCase    =				sigmas[step_indices].flatten()
												while len(sigma.shape      ) < len(original_samples.shape      ):
																		UpperCAmelCase    =				sigma.unsqueeze(-1      )

												UpperCAmelCase    =				original_samples + noise * sigma
												return noisy_samples






						def __len__(      self				:str      )       ->    Optional[Any]:
												return self.config.num_train_timesteps










"""simple docstring"""


import warnings

from .generation import FlaxGenerationMixin







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						# warning at import time
						warnings.warn(
						    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
						    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""			,     SCREAMING_SNAKE_CASE_			,     )





