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


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

	'''simple docstring'''



	lowerCamelCase_    :			torch.FloatTensor
	lowerCamelCase_    :			Optional[torch.FloatTensor]	  =       None



def     lowerCamelCase_    (  _lowercase					,	_lowercase=0.9_99					,	_lowercase="cosine"					,	)							->       str:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(_lowercase				):
						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2				) ** 2

		elif alpha_transform_type == "exp":

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

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

		__A							:    int         =      []
		for i in range(__lowerCamelCase				):
				__A							:    str         =      i / num_diffusion_timesteps
				__A							:    Union[str, Any]         =      (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 _a   (       __snake_case    ,							__snake_case					):

	'''simple docstring'''



	lowerCamelCase_    :			List[Any]	  =       [e.name for e in KarrasDiffusionSchedulers]
	lowerCamelCase_    :			List[Any]	  =       1

	@register_to_config
	def __init__(   self							, __UpperCAmelCase = 1_000							, __UpperCAmelCase = 0.00_01							, __UpperCAmelCase = 0.02							, __UpperCAmelCase = "linear"							, __UpperCAmelCase = None							, __UpperCAmelCase = "epsilon"							, __UpperCAmelCase = "linear"							, __UpperCAmelCase = False							, __UpperCAmelCase = "linspace"							, __UpperCAmelCase = 0							, ):
			if trained_betas is not None:
					__A							:    Any         =      torch.tensor(__UpperCAmelCase							, dtype=torch.floataa    )
			elif beta_schedule == "linear":
					__A							:    List[str]         =      torch.linspace(__UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, dtype=torch.floataa    )
			elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					__A							:    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
					__A							:    Optional[Any]         =      betas_for_alpha_bar(__UpperCAmelCase    )
			else:
					raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"    )

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

			__A							:    Dict         =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
			__A							:    Optional[int]         =      np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
			__A							:    Tuple         =      torch.from_numpy(__UpperCAmelCase    )

			# setable values
			__A							:    List[str]         =      None
			__A							:    List[Any]         =      np.linspace(0							, num_train_timesteps - 1							, __UpperCAmelCase							, dtype=__UpperCAmelCase    )[::-1].copy()
			__A							:    str         =      torch.from_numpy(__UpperCAmelCase    )
			__A							:    Tuple         =      False
			__A							:    int         =      use_karras_sigmas

	@property
	def      __UpperCAmelCase(   self    ):
			# 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							, __UpperCAmelCase							, __UpperCAmelCase    ):
			if isinstance(__UpperCAmelCase							, torch.Tensor    ):
					__A							:    List[str]         =      timestep.to(self.timesteps.device    )
			__A							:    Optional[Any]         =      (self.timesteps == timestep).nonzero().item()
			__A							:    int         =      self.sigmas[step_index]

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

			__A							:    Optional[Any]         =      True
			return sample

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

			__A							:    List[str]         =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
			__A							:    Optional[int]         =      np.log(__UpperCAmelCase    )

			if self.config.interpolation_type == "linear":
					__A							:    int         =      np.interp(__UpperCAmelCase							, np.arange(0							, len(__UpperCAmelCase    )    )							, __UpperCAmelCase    )
			elif self.config.interpolation_type == "log_linear":
					__A							:    List[str]         =      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:
					__A							:    Optional[int]         =      self._convert_to_karras(in_sigmas=__UpperCAmelCase							, num_inference_steps=self.num_inference_steps    )
					__A							:    Optional[Any]         =      np.array([self._sigma_to_t(__UpperCAmelCase							, __UpperCAmelCase    ) for sigma in sigmas]    )

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

	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase    ):
			# get log sigma
			__A							:    Optional[Any]         =      np.log(__UpperCAmelCase    )

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

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

			__A							:    int         =      log_sigmas[low_idx]
			__A							:    Union[str, Any]         =      log_sigmas[high_idx]

			# interpolate sigmas
			__A							:    Optional[int]         =      (low - log_sigma) / (low - high)
			__A							:    Dict         =      np.clip(__UpperCAmelCase							, 0							, 1    )

			# transform interpolation to time range
			__A							:    Tuple         =      (1 - w) * low_idx + w * high_idx
			__A							:    Optional[int]         =      t.reshape(sigma.shape    )
			return t

	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase    ):
			__A							:    int         =      in_sigmas[-1].item()
			__A							:    List[str]         =      in_sigmas[0].item()

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

	def      __UpperCAmelCase(   self							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase							, __UpperCAmelCase = 0.0							, __UpperCAmelCase = 0.0							, __UpperCAmelCase = float("inf"    )							, __UpperCAmelCase = 1.0							, __UpperCAmelCase = None							, __UpperCAmelCase = True							, ):

			if (
			    isinstance(__UpperCAmelCase							, __UpperCAmelCase    )
			    or isinstance(__UpperCAmelCase							, torch.IntTensor    )
			    or isinstance(__UpperCAmelCase							, 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(__UpperCAmelCase							, torch.Tensor    ):
					__A							:    Dict         =      timestep.to(self.timesteps.device    )

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

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

			__A							:    Optional[int]         =      randn_tensor(
			    model_output.shape							, dtype=model_output.dtype							, device=model_output.device							, generator=__UpperCAmelCase    )

			__A							:    Union[str, Any]         =      noise * s_noise
			__A							:    str         =      sigma * (gamma + 1)

			if gamma > 0:
					__A							:    List[Any]         =      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":
					__A							:    Dict         =      model_output
			elif self.config.prediction_type == "epsilon":
					__A							:    Optional[int]         =      sample - sigma_hat * model_output
			elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					__A							:    Optional[int]         =      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
			__A							:    Union[str, Any]         =      (sample - pred_original_sample) / sigma_hat

			__A							:    Optional[Any]         =      self.sigmas[step_index + 1] - sigma_hat

			__A							:    int         =      sample + derivative * dt

			if not return_dict:
					return (prev_sample,)

			return EulerDiscreteSchedulerOutput(prev_sample=__UpperCAmelCase							, pred_original_sample=__UpperCAmelCase    )

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

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

			__A							:    List[Any]         =      sigmas[step_indices].flatten()
			while len(sigma.shape    ) < len(original_samples.shape    ):
					__A							:    Tuple         =      sigma.unsqueeze(-1    )

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







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






from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


lowerCamelCase_				:		List[Any]       						=       TypeVar("""DatasetType""", Dataset, IterableDataset)
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					if isinstance(__lowerCamelCase      ,				__lowerCamelCase						):
										return _split_by_node_map_style_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)
					else:
										return _split_by_node_iterable_dataset(__lowerCamelCase      ,				rank=__lowerCamelCase      ,				world_size=__lowerCamelCase						)




