




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 		__A		(	__lowerCamelCase							,			__lowerCamelCase=0.999							,			__lowerCamelCase="cosine"							,			)      ->       Dict:
	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}'    )

	a										=							[]
	for i in range(__lowerCamelCase    ):
		a										=							i / num_diffusion_timesteps
		a										=							(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	(	__magic_name__      ,       __magic_name__ ):
							UpperCamelCase__					     =  [e.name for e in KarrasDiffusionSchedulers]
							UpperCamelCase__					     =  2
							@register_to_config
							def __init__(						self						:str		,      __magic_name__						:int = 1000		,      __magic_name__						:float = 0.00085		,      __magic_name__						:float = 0.012		,      __magic_name__						:str = "linear"		,      __magic_name__						:Optional[Union[np.ndarray, List[float]]] = None		,      __magic_name__						:str = "epsilon"		,      __magic_name__						:str = "linspace"		,      __magic_name__						:int = 0		,      ):







								'''simple docstring'''







								if trained_betas is not None:
									a										=							torch.tensor(__magic_name__		,      dtype=torch.floataa				)
								elif beta_schedule == "linear":
									a										=							torch.linspace(__magic_name__		,      __magic_name__		,      __magic_name__		,      dtype=torch.floataa				)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									a										=							(
									    torch.linspace(beta_start**0.5		,      beta_end**0.5		,      __magic_name__		,      dtype=torch.floataa				) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									a										=							betas_for_alpha_bar(__magic_name__				)
								else:
									raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'				)

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

								#  set all values
								self.set_timesteps(__magic_name__		,      __magic_name__		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:Tuple		,      __magic_name__						:int=None				):







								'''simple docstring'''







								if schedule_timesteps is None:
									a										=							self.timesteps

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

								return indices[pos].item()
							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5
							def       lowerCamelCase__							(						self						:Any		,      __magic_name__						:torch.FloatTensor		,      __magic_name__						:Union[float, torch.FloatTensor]		,      ):







								'''simple docstring'''







								a										=							self.index_for_timestep(__magic_name__				)

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

								a										=							sample / ((sigma**2 + 1) ** 0.5)
								return sample
							def       lowerCamelCase__							(						self						:Optional[int]		,      __magic_name__						:int		,      __magic_name__						:Union[str, torch.device] = None		,      __magic_name__						:Optional[int] = None		,      ):







								'''simple docstring'''







								a										=							num_inference_steps

								a										=							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":
									a										=							np.linspace(0		,      num_train_timesteps - 1		,      __magic_name__		,      dtype=__magic_name__				)[::-1].copy()
								elif self.config.timestep_spacing == "leading":
									a										=							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										=							(np.arange(0		,      __magic_name__				) * step_ratio).round()[::-1].copy().astype(__magic_name__				)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									a										=							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										=							(np.arange(__magic_name__		,      0		,      -step_ratio				)).round().copy().astype(__magic_name__				)
									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										=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
								a										=							torch.from_numpy(np.log(__magic_name__				)				).to(__magic_name__				)

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

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

								a										=							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
								a										=							torch.cat(
								    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)

								if str(__magic_name__				).startswith("""mps"""				):
									# mps does not support float64
									a										=							torch.from_numpy(__magic_name__				).to(__magic_name__		,      dtype=torch.floataa				)
								else:
									a										=							torch.from_numpy(__magic_name__				).to(__magic_name__				)

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

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

								a										=							None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								a										=							defaultdict(__magic_name__				)
							def       lowerCamelCase__							(						self						:Union[str, Any]		,      __magic_name__						:Dict				):







								'''simple docstring'''







								a										=							sigma.log()

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

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

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

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

								# transform interpolation to time range
								a										=							(1 - w) * low_idx + w * high_idx
								a										=							t.view(sigma.shape				)
								return t
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								return self.sample is None
							def       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:Union[torch.FloatTensor, np.ndarray]		,      __magic_name__						:Union[float, torch.FloatTensor]		,      __magic_name__						:Union[torch.FloatTensor, np.ndarray]		,      __magic_name__						:bool = True		,      ):







								'''simple docstring'''







								a										=							self.index_for_timestep(__magic_name__				)

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

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

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

									# 3. delta timestep
									a										=							sigma_next - sigma_hat

									a										=							self.sample
									a										=							None

								a										=							sample + derivative * dt

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=__magic_name__				)
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:torch.FloatTensor		,      __magic_name__						:torch.FloatTensor		,      __magic_name__						:torch.FloatTensor		,      ):







								'''simple docstring'''







								a										=							self.sigmas.to(device=original_samples.device		,      dtype=original_samples.dtype				)
								if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__				):
									# mps does not support float64
									a										=							self.timesteps.to(original_samples.device		,      dtype=torch.floataa				)
									a										=							timesteps.to(original_samples.device		,      dtype=torch.floataa				)
								else:
									a										=							self.timesteps.to(original_samples.device				)
									a										=							timesteps.to(original_samples.device				)

								a										=							[self.index_for_timestep(__magic_name__		,      __magic_name__				) for t in timesteps]

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

								a										=							original_samples + noise * sigma
								return noisy_samples





							def __len__(						self						:List[str]				):







								'''simple docstring'''







								return self.config.num_train_timesteps







__UpperCamelCase					:       Optional[int]    					=      {
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}

