
"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase=0.999	,  _UpperCamelCase="cosine"	,  ):
	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

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

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

	__lowerCAmelCase      :						Dict						      =				[]
	for i in range(_UpperCamelCase		):
		__lowerCAmelCase      :						Tuple						      =				i / num_diffusion_timesteps
		__lowerCAmelCase      :						Optional[int]						      =				(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(_UpperCamelCase		) / alpha_bar_fn(_UpperCamelCase		)	,  _UpperCamelCase		)		)
	return torch.tensor(_UpperCamelCase	,  dtype=torch.floataa		)




class 			A__	(     _lowerCamelCase	,					_lowerCamelCase):
		A_						:	Optional[int]           =     [e.name for e in KarrasDiffusionSchedulers]
		A_						:	Optional[Any]           =     2

		@register_to_config
		def __init__(				self	,		_SCREAMING_SNAKE_CASE = 10_00	,		_SCREAMING_SNAKE_CASE = 0.0_0085	,		_SCREAMING_SNAKE_CASE = 0.012	,		_SCREAMING_SNAKE_CASE = "linear"	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = "epsilon"	,		_SCREAMING_SNAKE_CASE = "linspace"	,		_SCREAMING_SNAKE_CASE = 0	,		):
			if trained_betas is not None:
				__lowerCAmelCase      :						int						      =				torch.tensor(_SCREAMING_SNAKE_CASE	,		dtype=torch.floataa			)
			elif beta_schedule == "linear":
				__lowerCAmelCase      :						Any						      =				torch.linspace(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		dtype=torch.floataa			)
			elif beta_schedule == "scaled_linear":
				# this schedule is very specific to the latent diffusion model.
				__lowerCAmelCase      :						str						      =				(
				    torch.linspace(beta_start**0.5	,		beta_end**0.5	,		_SCREAMING_SNAKE_CASE	,		dtype=torch.floataa			) ** 2
				)
			elif beta_schedule == "squaredcos_cap_v2":
				# Glide cosine schedule
				__lowerCAmelCase      :						Optional[Any]						      =				betas_for_alpha_bar(_SCREAMING_SNAKE_CASE			)
			else:
				raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"			)

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

			#  set all values
			self.set_timesteps(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None			):
			if schedule_timesteps is None:
				__lowerCAmelCase      :						Union[str, Any]						      =				self.timesteps

			__lowerCAmelCase      :						Dict						      =				(schedule_timesteps == timestep).nonzero()

			# The sigma index that is taken for the **very** first `step`
			# is always the second index (or the last index if there is only 1)
			# This way we can ensure we don't accidentally skip a sigma in
			# case we start in the middle of the denoising schedule (e.g. for image-to-image)
			if len(self._index_counter			) == 0:
				__lowerCAmelCase      :						Dict						      =				1 if len(_SCREAMING_SNAKE_CASE			) > 1 else 0
			else:
				__lowerCAmelCase      :						Optional[int]						      =				timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE			) else timestep
				__lowerCAmelCase      :						Optional[Any]						      =				self._index_counter[timestep_int]

			return indices[pos].item()

		@property
		def 							__lowerCamelCase							(				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 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		):
			__lowerCAmelCase      :						Dict						      =				self.index_for_timestep(_SCREAMING_SNAKE_CASE			)

			if self.state_in_first_order:
				__lowerCAmelCase      :						str						      =				self.sigmas[step_index]
			else:
				__lowerCAmelCase      :						Optional[int]						      =				self.sigmas_interpol[step_index]

			__lowerCAmelCase      :						int						      =				sample / ((sigma**2 + 1) ** 0.5)
			return sample

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		):
			__lowerCAmelCase      :						List[Any]						      =				num_inference_steps

			__lowerCAmelCase      :						List[Any]						      =				num_train_timesteps or self.config.num_train_timesteps

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

			__lowerCAmelCase      :						Optional[Any]						      =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
			__lowerCAmelCase      :						int						      =				torch.from_numpy(np.log(_SCREAMING_SNAKE_CASE			)			).to(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				np.interp(_SCREAMING_SNAKE_CASE	,		np.arange(0	,		len(_SCREAMING_SNAKE_CASE			)			)	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
			__lowerCAmelCase      :						Optional[Any]						      =				torch.from_numpy(_SCREAMING_SNAKE_CASE			).to(device=_SCREAMING_SNAKE_CASE			)

			# interpolate sigmas
			__lowerCAmelCase      :						Optional[Any]						      =				sigmas.log().lerp(sigmas.roll(1			).log()	,		0.5			).exp()

			__lowerCAmelCase      :						int						      =				torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
			__lowerCAmelCase      :						Union[str, Any]						      =				torch.cat(
			    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2			), sigmas_interpol[-1:]]			)

			if str(_SCREAMING_SNAKE_CASE			).startswith('mps'			):
				# mps does not support float64
				__lowerCAmelCase      :						Optional[int]						      =				torch.from_numpy(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE	,		dtype=torch.floataa			)
			else:
				__lowerCAmelCase      :						Tuple						      =				torch.from_numpy(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			)

			# interpolate timesteps
			__lowerCAmelCase      :						str						      =				self.sigma_to_t(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE	,		dtype=timesteps.dtype			)
			__lowerCAmelCase      :						Optional[Any]						      =				torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])	,		dim=-1			).flatten()

			__lowerCAmelCase      :						Optional[int]						      =				torch.cat([timesteps[:1], interleaved_timesteps]			)

			__lowerCAmelCase      :						Optional[int]						      =				None

			# for exp beta schedules, such as the one for `pipeline_shap_e.py`
			# we need an index counter
			__lowerCAmelCase      :						List[Any]						      =				defaultdict(_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			# get log sigma
			__lowerCAmelCase      :						Tuple						      =				sigma.log()

			# get distribution
			__lowerCAmelCase      :						List[str]						      =				log_sigma - self.log_sigmas[:, None]

			# get sigmas range
			__lowerCAmelCase      :						List[Any]						      =				dists.ge(0			).cumsum(dim=0			).argmax(dim=0			).clamp(max=self.log_sigmas.shape[0] - 2			)
			__lowerCAmelCase      :						Dict						      =				low_idx + 1

			__lowerCAmelCase      :						int						      =				self.log_sigmas[low_idx]
			__lowerCAmelCase      :						Tuple						      =				self.log_sigmas[high_idx]

			# interpolate sigmas
			__lowerCAmelCase      :						List[Any]						      =				(low - log_sigma) / (low - high)
			__lowerCAmelCase      :						Optional[int]						      =				w.clamp(0	,		1			)

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

		@property
		def 							__lowerCamelCase							(				self			):
			return self.sample is None

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = True	,		):
			__lowerCAmelCase      :						Any						      =				self.index_for_timestep(_SCREAMING_SNAKE_CASE			)

			# advance index counter by 1
			__lowerCAmelCase      :						Any						      =				timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE			) else timestep
			self._index_counter[timestep_int] += 1

			if self.state_in_first_order:
				__lowerCAmelCase      :						Optional[Any]						      =				self.sigmas[step_index]
				__lowerCAmelCase      :						int						      =				self.sigmas_interpol[step_index + 1]
				__lowerCAmelCase      :						List[Any]						      =				self.sigmas[step_index + 1]
			else:
				# 2nd order / KDPM2's method
				__lowerCAmelCase      :						Optional[int]						      =				self.sigmas[step_index - 1]
				__lowerCAmelCase      :						Optional[Any]						      =				self.sigmas_interpol[step_index]
				__lowerCAmelCase      :						Tuple						      =				self.sigmas[step_index]

			# currently only gamma=0 is supported. This usually works best anyways.
			# We can support gamma in the future but then need to scale the timestep before
			# passing it to the model which requires a change in API
			__lowerCAmelCase      :						List[Any]						      =				0
			__lowerCAmelCase      :						Union[str, Any]						      =				sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

			# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
			if self.config.prediction_type == "epsilon":
				__lowerCAmelCase      :						Dict						      =				sigma_hat if self.state_in_first_order else sigma_interpol
				__lowerCAmelCase      :						Dict						      =				sample - sigma_input * model_output
			elif self.config.prediction_type == "v_prediction":
				__lowerCAmelCase      :						Any						      =				sigma_hat if self.state_in_first_order else sigma_interpol
				__lowerCAmelCase      :						Union[str, Any]						      =				model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
				    sample / (sigma_input**2 + 1)
				)
			elif self.config.prediction_type == "sample":
				raise NotImplementedError('prediction_type not implemented yet: sample'			)
			else:
				raise ValueError(
				    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"			)

			if self.state_in_first_order:
				# 2. Convert to an ODE derivative for 1st order
				__lowerCAmelCase      :						List[str]						      =				(sample - pred_original_sample) / sigma_hat
				# 3. delta timestep
				__lowerCAmelCase      :						Tuple						      =				sigma_interpol - sigma_hat

				# store for 2nd order step
				__lowerCAmelCase      :						Optional[int]						      =				sample
			else:
				# DPM-Solver-2
				# 2. Convert to an ODE derivative for 2nd order
				__lowerCAmelCase      :						Union[str, Any]						      =				(sample - pred_original_sample) / sigma_interpol

				# 3. delta timestep
				__lowerCAmelCase      :						Optional[Any]						      =				sigma_next - sigma_hat

				__lowerCAmelCase      :						int						      =				self.sample
				__lowerCAmelCase      :						Optional[int]						      =				None

			__lowerCAmelCase      :						Optional[int]						      =				sample + derivative * dt

			if not return_dict:
				return (prev_sample,)

			return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE			)

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

			__lowerCAmelCase      :						List[Any]						      =				[self.index_for_timestep(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) for t in timesteps]

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

			__lowerCAmelCase      :						Tuple						      =				original_samples + noise * sigma
			return noisy_samples







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

"""simple docstring"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)