


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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 							UpperCamelCase_  (				__snake_case			:						Any	,			__snake_case			:						Optional[int]=0.999	,			__snake_case			:						List[str]="cosine"	,			)->      Optional[int]:




						'''simple docstring'''
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(__snake_case			:						List[str]     ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(__snake_case			:						Union[str, Any]     ):
																		return math.exp(t * -12.0     )

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

						__magic_name__ 		=			[]
						for i in range(__snake_case     ):
												__magic_name__ 		=			i / num_diffusion_timesteps
												__magic_name__ 		=			(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     __snake_case							(      _UpperCAmelCase  ,			_UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					List[str] 				= [e.name for e in KarrasDiffusionSchedulers]
					SCREAMING_SNAKE_CASE_		:					List[str] 				= 2
					@register_to_config
					def __init__(	self					:	int  ,			_lowerCAmelCase					:	int = 1_0_0_0  ,			_lowerCAmelCase					:	float = 0.0_0085  ,			_lowerCAmelCase					:	float = 0.012  ,			_lowerCAmelCase					:	str = "linear"  ,			_lowerCAmelCase					:	Optional[Union[np.ndarray, List[float]]] = None  ,			_lowerCAmelCase					:	str = "epsilon"  ,			_lowerCAmelCase					:	str = "linspace"  ,			_lowerCAmelCase					:	int = 0  ,			)       ->				int:



											'''simple docstring'''





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

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

											#  set all values
											self.set_timesteps(_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase     )
					def   a (	self					:	Optional[Any]  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	str=None     )       ->				Optional[int]:



											'''simple docstring'''





											if schedule_timesteps is None:
																	__magic_name__ 		=			self.timesteps

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

											return indices[pos].item()
					@property
					def   a (	self					:	List[str]     )       ->				Tuple:



											'''simple docstring'''





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

											return (self.sigmas.max() ** 2 + 1) ** 0.5
					def   a (	self					:	str  ,			_lowerCAmelCase					:	torch.FloatTensor  ,			_lowerCAmelCase					:	Union[float, torch.FloatTensor]  ,			)       ->				torch.FloatTensor:



											'''simple docstring'''





											__magic_name__ 		=			self.index_for_timestep(_lowerCAmelCase     )

											if self.state_in_first_order:
																	__magic_name__ 		=			self.sigmas[step_index]
											else:
																	__magic_name__ 		=			self.sigmas_interpol[step_index - 1]

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



											'''simple docstring'''





											__magic_name__ 		=			num_inference_steps

											__magic_name__ 		=			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":
																	__magic_name__ 		=			np.linspace(0  ,			num_train_timesteps - 1  ,			_lowerCAmelCase  ,			dtype=_lowerCAmelCase     )[::-1].copy()
											elif self.config.timestep_spacing == "leading":
																	__magic_name__ 		=			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
																	__magic_name__ 		=			(np.arange(0  ,			_lowerCAmelCase     ) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase     )
																	timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																	__magic_name__ 		=			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
																	__magic_name__ 		=			(np.arange(_lowerCAmelCase  ,			0  ,			-step_ratio     )).round().copy().astype(_lowerCAmelCase     )
																	timesteps -= 1
											else:
																	raise ValueError(
																	    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''     )

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

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

											# compute up and down sigmas
											__magic_name__ 		=			sigmas.roll(-1     )
											__magic_name__ 		=			0.0
											__magic_name__ 		=			(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
											__magic_name__ 		=			(sigmas_next**2 - sigmas_up**2) ** 0.5
											__magic_name__ 		=			0.0

											# compute interpolated sigmas
											__magic_name__ 		=			sigmas.log().lerp(sigmas_down.log()  ,			0.5     ).exp()
											__magic_name__ 		=			0.0

											# set sigmas
											__magic_name__ 		=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2     ), sigmas[-1:]]     )
											__magic_name__ 		=			torch.cat(
											    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2     ), sigmas_interpol[-1:]]     )
											__magic_name__ 		=			torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2     ), sigmas_up[-1:]]     )
											__magic_name__ 		=			torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2     ), sigmas_down[-1:]]     )

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

											__magic_name__ 		=			self.sigma_to_t(_lowerCAmelCase     ).to(_lowerCAmelCase  ,			dtype=timesteps.dtype     )
											__magic_name__ 		=			torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])  ,			dim=-1     ).flatten()

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

											__magic_name__ 		=			None

											# for exp beta schedules, such as the one for `pipeline_shap_e.py`
											# we need an index counter
											__magic_name__ 		=			defaultdict(_lowerCAmelCase     )
					def   a (	self					:	List[str]  ,			_lowerCAmelCase					:	int     )       ->				Optional[int]:



											'''simple docstring'''





											__magic_name__ 		=			sigma.log()

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

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

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

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

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



											'''simple docstring'''





											return self.sample is None
					def   a (	self					:	str  ,			_lowerCAmelCase					:	Union[torch.FloatTensor, np.ndarray]  ,			_lowerCAmelCase					:	Union[float, torch.FloatTensor]  ,			_lowerCAmelCase					:	Union[torch.FloatTensor, np.ndarray]  ,			_lowerCAmelCase					:	Optional[torch.Generator] = None  ,			_lowerCAmelCase					:	bool = True  ,			)       ->				Union[SchedulerOutput, Tuple]:



											'''simple docstring'''





											__magic_name__ 		=			self.index_for_timestep(_lowerCAmelCase     )

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

											if self.state_in_first_order:
																	__magic_name__ 		=			self.sigmas[step_index]
																	__magic_name__ 		=			self.sigmas_interpol[step_index]
																	__magic_name__ 		=			self.sigmas_up[step_index]
																	__magic_name__ 		=			self.sigmas_down[step_index - 1]
											else:
																	# 2nd order / KPDM2's method
																	__magic_name__ 		=			self.sigmas[step_index - 1]
																	__magic_name__ 		=			self.sigmas_interpol[step_index - 1]
																	__magic_name__ 		=			self.sigmas_up[step_index - 1]
																	__magic_name__ 		=			self.sigmas_down[step_index - 1]

											# 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
											__magic_name__ 		=			0
											__magic_name__ 		=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

											__magic_name__ 		=			model_output.device
											__magic_name__ 		=			randn_tensor(model_output.shape  ,			dtype=model_output.dtype  ,			device=_lowerCAmelCase  ,			generator=_lowerCAmelCase     )

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																	__magic_name__ 		=			sigma_hat if self.state_in_first_order else sigma_interpol
																	__magic_name__ 		=			sample - sigma_input * model_output
											elif self.config.prediction_type == "v_prediction":
																	__magic_name__ 		=			sigma_hat if self.state_in_first_order else sigma_interpol
																	__magic_name__ 		=			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
																	__magic_name__ 		=			(sample - pred_original_sample) / sigma_hat
																	# 3. delta timestep
																	__magic_name__ 		=			sigma_interpol - sigma_hat

																	# store for 2nd order step
																	__magic_name__ 		=			sample
																	__magic_name__ 		=			dt
																	__magic_name__ 		=			sample + derivative * dt
											else:
																	# DPM-Solver-2
																	# 2. Convert to an ODE derivative for 2nd order
																	__magic_name__ 		=			(sample - pred_original_sample) / sigma_interpol
																	# 3. delta timestep
																	__magic_name__ 		=			sigma_down - sigma_hat

																	__magic_name__ 		=			self.sample
																	__magic_name__ 		=			None

																	__magic_name__ 		=			sample + derivative * dt
																	__magic_name__ 		=			prev_sample + noise * sigma_up

											if not return_dict:
																	return (prev_sample,)

											return SchedulerOutput(prev_sample=_lowerCAmelCase     )
					def   a (	self					:	str  ,			_lowerCAmelCase					:	torch.FloatTensor  ,			_lowerCAmelCase					:	torch.FloatTensor  ,			_lowerCAmelCase					:	torch.FloatTensor  ,			)       ->				torch.FloatTensor:



											'''simple docstring'''





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

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

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

											__magic_name__ 		=			original_samples + noise * sigma
											return noisy_samples







					def __len__(	self					:	Optional[Any]     )       ->				Tuple:



											'''simple docstring'''





											return self.config.num_train_timesteps





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:					Tuple 				= "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:					Dict 				= "AutoTokenizer"
					def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			**_lowerCAmelCase					:	List[str]     )       ->				Optional[int]:



											'''simple docstring'''





											__magic_name__ 		=			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'  ,			_lowerCAmelCase  ,			)
																	__magic_name__ 		=			kwargs.pop('feature_extractor'     )

											__magic_name__ 		=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You have to specify an image_processor.'     )
											if tokenizer is None:
																	raise ValueError('You have to specify a tokenizer.'     )

											super().__init__(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			self.image_processor
					def __call__(	self					:	str  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	int=None  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	int     )       ->				int:



											'''simple docstring'''





											if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																	__magic_name__ 		=			self.tokenizer(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if images is not None:
																	__magic_name__ 		=			self.image_processor(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if text is not None and images is not None:
																	__magic_name__ 		=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**_lowerCAmelCase     )  ,			tensor_type=_lowerCAmelCase     )
					def   a (	self					:	Optional[int]  ,			*_lowerCAmelCase					:	Optional[int]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]  ,			*_lowerCAmelCase					:	str  ,			**_lowerCAmelCase					:	Tuple     )       ->				int:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					@property
					def   a (	self					:	List[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer.model_input_names
											__magic_name__ 		=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
					@property
					def   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor_class







					@property
					def   a (	self					:	Dict     )       ->				Tuple:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor

