




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        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case=0.999			,     snake_case="cosine"			,     )						->				Tuple:
						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

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




class       _A				(		__lowercase       ,     __lowercase     ):
		__a					    =       [e.name for e in KarrasDiffusionSchedulers]
		__a					    =       2






		@register_to_config
		def __init__(    self						,		_SCREAMING_SNAKE_CASE = 1000						,		_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:
														_UpperCAmelCase      =			torch.tensor(_SCREAMING_SNAKE_CASE						,		dtype=torch.floataa			)
								elif beta_schedule == "linear":
														_UpperCAmelCase      =			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.
														_UpperCAmelCase      =			(
														    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
														_UpperCAmelCase      =			betas_for_alpha_bar(_SCREAMING_SNAKE_CASE			)
								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			)

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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								if schedule_timesteps is None:
														_UpperCAmelCase      =			self.timesteps

								_UpperCAmelCase      =			(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:
														_UpperCAmelCase      =			1 if len(_SCREAMING_SNAKE_CASE			) > 1 else 0
								else:
														_UpperCAmelCase      =			timestep.cpu().item() if torch.is_tensor(_SCREAMING_SNAKE_CASE			) else timestep
														_UpperCAmelCase      =			self._index_counter[timestep_int]

								return indices[pos].item()






		@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						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		):
								_UpperCAmelCase      =			self.index_for_timestep(_SCREAMING_SNAKE_CASE			)

								if self.state_in_first_order:
														_UpperCAmelCase      =			self.sigmas[step_index]
								else:
														_UpperCAmelCase      =			self.sigmas_interpol[step_index]

								_UpperCAmelCase      =			sample / ((sigma**2 + 1) ** 0.5)
								return sample






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		):
								_UpperCAmelCase      =			num_inference_steps

								_UpperCAmelCase      =			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":
														_UpperCAmelCase      =			np.linspace(0						,		num_train_timesteps - 1						,		_SCREAMING_SNAKE_CASE						,		dtype=_SCREAMING_SNAKE_CASE			)[::-1].copy()
								elif self.config.timestep_spacing == "leading":
														_UpperCAmelCase      =			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						,		_SCREAMING_SNAKE_CASE			) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE			)
														timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
														_UpperCAmelCase      =			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(_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'."			)

								_UpperCAmelCase      =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
								_UpperCAmelCase      =			torch.from_numpy(np.log(_SCREAMING_SNAKE_CASE			)			).to(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.interp(_SCREAMING_SNAKE_CASE						,		np.arange(0						,		len(_SCREAMING_SNAKE_CASE			)			)						,		_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
								_UpperCAmelCase      =			torch.from_numpy(_SCREAMING_SNAKE_CASE			).to(device=_SCREAMING_SNAKE_CASE			)

								# interpolate sigmas
								_UpperCAmelCase      =			sigmas.log().lerp(sigmas.roll(1			).log()						,		0.5			).exp()

								_UpperCAmelCase      =			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2			), sigmas[-1:]]			)
								_UpperCAmelCase      =			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
														_UpperCAmelCase      =			torch.from_numpy(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE						,		dtype=torch.floataa			)
								else:
														_UpperCAmelCase      =			torch.from_numpy(_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			)

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

								_UpperCAmelCase      =			torch.cat([timesteps[:1], interleaved_timesteps]			)

								_UpperCAmelCase      =			None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								_UpperCAmelCase      =			defaultdict(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								# get log sigma
								_UpperCAmelCase      =			sigma.log()

								# get distribution
								_UpperCAmelCase      =			log_sigma - self.log_sigmas[:, None]

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

								_UpperCAmelCase      =			self.log_sigmas[low_idx]
								_UpperCAmelCase      =			self.log_sigmas[high_idx]

								# interpolate sigmas
								_UpperCAmelCase      =			(low - log_sigma) / (low - high)
								_UpperCAmelCase      =			w.clamp(0						,		1			)

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






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






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = True						,		):
								_UpperCAmelCase      =			self.index_for_timestep(_SCREAMING_SNAKE_CASE			)

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

								if self.state_in_first_order:
														_UpperCAmelCase      =			self.sigmas[step_index]
														_UpperCAmelCase      =			self.sigmas_interpol[step_index + 1]
														_UpperCAmelCase      =			self.sigmas[step_index + 1]
								else:
														# 2nd order / KDPM2's method
														_UpperCAmelCase      =			self.sigmas[step_index - 1]
														_UpperCAmelCase      =			self.sigmas_interpol[step_index]
														_UpperCAmelCase      =			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
								_UpperCAmelCase      =			0
								_UpperCAmelCase      =			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":
														_UpperCAmelCase      =			sigma_hat if self.state_in_first_order else sigma_interpol
														_UpperCAmelCase      =			sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
														_UpperCAmelCase      =			sigma_hat if self.state_in_first_order else sigma_interpol
														_UpperCAmelCase      =			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
														_UpperCAmelCase      =			(sample - pred_original_sample) / sigma_hat
														# 3. delta timestep
														_UpperCAmelCase      =			sigma_interpol - sigma_hat

														# store for 2nd order step
														_UpperCAmelCase      =			sample
								else:
														# DPM-Solver-2
														# 2. Convert to an ODE derivative for 2nd order
														_UpperCAmelCase      =			(sample - pred_original_sample) / sigma_interpol

														# 3. delta timestep
														_UpperCAmelCase      =			sigma_next - sigma_hat

														_UpperCAmelCase      =			self.sample
														_UpperCAmelCase      =			None

								_UpperCAmelCase      =			sample + derivative * dt

								if not return_dict:
														return (prev_sample,)

								return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		):
								# 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(_SCREAMING_SNAKE_CASE			):
														# 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      =			[self.index_for_timestep(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			) 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			):
								return self.config.num_train_timesteps





from typing import Dict

from .base import GenericTensor, Pipeline




class       _A				(		__lowercase     ):






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								if tokenize_kwargs is None:
														_UpperCAmelCase      =			{}

								if truncation is not None:
														if "truncation" in tokenize_kwargs:
																				raise ValueError(
																				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""			)
														_UpperCAmelCase      =			truncation

								_UpperCAmelCase      =			tokenize_kwargs

								_UpperCAmelCase      =			{}
								if return_tensors is not None:
														_UpperCAmelCase      =			return_tensors

								return preprocess_params, {}, postprocess_params






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.framework
								_UpperCAmelCase      =			self.tokenizer(_SCREAMING_SNAKE_CASE						,		return_tensors=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								return model_inputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.model(**_SCREAMING_SNAKE_CASE			)
								return model_outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
														return model_outputs[0]
								if self.framework == "pt":
														return model_outputs[0].tolist()
								elif self.framework == "tf":
														return model_outputs[0].numpy().tolist()






		def __call__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								return super().__call__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)