


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__			(				A__,      A__=0.9_99,      A__="cosine",      ):
			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

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

			SCREAMING_SNAKE_CASE_		:     str					=					[]
			for i in range(A__				):
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					i / num_diffusion_timesteps
						SCREAMING_SNAKE_CASE_		:     Dict					=					(i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(A__				) / alpha_bar_fn(A__				),      A__				)				)
			return torch.tensor(A__,      dtype=torch.floataa				)







class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						[e.name for e in KarrasDiffusionSchedulers]
							_UpperCAmelCase      =						2






							@register_to_config
							def __init__(      self			,						lowerCAmelCase__ = 1_0_0_0			,						lowerCAmelCase__ = 0.00_085			,						lowerCAmelCase__ = 0.012			,						lowerCAmelCase__ = "linear"			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = "epsilon"			,						lowerCAmelCase__ = "linspace"			,						lowerCAmelCase__ = 0			,						):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					1.0 - self.betas
										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.cumprod(self.alphas			,						dim=0       )

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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=None       ):





										"""simple docstring"""




										if schedule_timesteps is None:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.timesteps

										SCREAMING_SNAKE_CASE_		:     Any					=					(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:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					1 if len(lowerCAmelCase__       ) > 1 else 0
										else:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__       ) else timestep
													SCREAMING_SNAKE_CASE_		:     Dict					=					self._index_counter[timestep_int]

										return indices[pos].item()






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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

										return (self.sigmas.max() ** 2 + 1) ** 0.5






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.index_for_timestep(lowerCAmelCase__       )

										if self.state_in_first_order:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.sigmas[step_index]
										else:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.sigmas_interpol[step_index]

										SCREAMING_SNAKE_CASE_		:     Any					=					sample / ((sigma**2 + 1) ** 0.5)
										return sample






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					num_inference_steps

										SCREAMING_SNAKE_CASE_		:     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":
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					np.linspace(0			,						num_train_timesteps - 1			,						lowerCAmelCase__			,						dtype=lowerCAmelCase__       )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
													SCREAMING_SNAKE_CASE_		:     Tuple					=					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
													SCREAMING_SNAKE_CASE_		:     Any					=					(np.arange(0			,						lowerCAmelCase__       ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase__       )
													timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
													SCREAMING_SNAKE_CASE_		:     Optional[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
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(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\'.'''       )

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

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					np.interp(lowerCAmelCase__			,						np.arange(0			,						len(lowerCAmelCase__       )       )			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					torch.from_numpy(lowerCAmelCase__       ).to(device=lowerCAmelCase__       )

										# interpolate sigmas
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					sigmas.log().lerp(sigmas.roll(1       ).log()			,						0.5       ).exp()

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

										if str(lowerCAmelCase__       ).startswith('mps'       ):
													# mps does not support float64
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.from_numpy(lowerCAmelCase__       ).to(lowerCAmelCase__			,						dtype=torch.floataa       )
										else:
													SCREAMING_SNAKE_CASE_		:     str					=					torch.from_numpy(lowerCAmelCase__       ).to(lowerCAmelCase__       )

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

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

										SCREAMING_SNAKE_CASE_		:     List[str]					=					None

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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					sigma.log()

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

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

										SCREAMING_SNAKE_CASE_		:     Dict					=					self.log_sigmas[low_idx]
										SCREAMING_SNAKE_CASE_		:     Any					=					self.log_sigmas[high_idx]

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

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






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self.sample is None






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__ = True			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.index_for_timestep(lowerCAmelCase__       )

										# advance index counter by 1
										SCREAMING_SNAKE_CASE_		:     List[str]					=					timestep.cpu().item() if torch.is_tensor(lowerCAmelCase__       ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.sigmas[step_index]
													SCREAMING_SNAKE_CASE_		:     Dict					=					self.sigmas_interpol[step_index + 1]
													SCREAMING_SNAKE_CASE_		:     str					=					self.sigmas[step_index + 1]
										else:
													# 2nd order / KDPM2's method
													SCREAMING_SNAKE_CASE_		:     int					=					self.sigmas[step_index - 1]
													SCREAMING_SNAKE_CASE_		:     Tuple					=					self.sigmas_interpol[step_index]
													SCREAMING_SNAKE_CASE_		:     int					=					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
										SCREAMING_SNAKE_CASE_		:     str					=					0
										SCREAMING_SNAKE_CASE_		:     int					=					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":
													SCREAMING_SNAKE_CASE_		:     Tuple					=					sigma_hat if self.state_in_first_order else sigma_interpol
													SCREAMING_SNAKE_CASE_		:     str					=					sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					sigma_hat if self.state_in_first_order else sigma_interpol
													SCREAMING_SNAKE_CASE_		:     List[str]					=					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
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					sigma_interpol - sigma_hat

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

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

													SCREAMING_SNAKE_CASE_		:     str					=					self.sample
													SCREAMING_SNAKE_CASE_		:     int					=					None

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					sample + derivative * dt

										if not return_dict:
													return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					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
													SCREAMING_SNAKE_CASE_		:     List[str]					=					self.timesteps.to(original_samples.device			,						dtype=torch.floataa       )
													SCREAMING_SNAKE_CASE_		:     str					=					timesteps.to(original_samples.device			,						dtype=torch.floataa       )
										else:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					self.timesteps.to(original_samples.device       )
													SCREAMING_SNAKE_CASE_		:     List[str]					=					timesteps.to(original_samples.device       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[self.index_for_timestep(lowerCAmelCase__			,						lowerCAmelCase__       ) for t in timesteps]

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

										SCREAMING_SNAKE_CASE_		:     List[str]					=					original_samples + noise * sigma
										return noisy_samples






							def __len__(      self       ):





										"""simple docstring"""




										return self.config.num_train_timesteps









from ...processing_utils import ProcessorMixin







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""WhisperFeatureExtractor"""
							_UpperCAmelCase      =						"""WhisperTokenizer"""






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.feature_extractor
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					False






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=None			,						lowerCAmelCase__=True       ):





										"""simple docstring"""




										return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase__			,						language=lowerCAmelCase__			,						no_timestamps=lowerCAmelCase__       )






							def __call__(      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										if self._in_target_context_manager:
													return self.current_processor(*lowerCAmelCase__			,						**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					kwargs.pop('audio'			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					kwargs.pop('sampling_rate'			,						lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					kwargs.pop('text'			,						lowerCAmelCase__       )
										if len(lowerCAmelCase__       ) > 0:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					args[0]
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					args[1:]

										if audio is None and text is None:
													raise ValueError('You need to specify either an `audio` or `text` input to process.'       )

										if audio is not None:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.feature_extractor(lowerCAmelCase__			,						*lowerCAmelCase__			,						sampling_rate=lowerCAmelCase__			,						**lowerCAmelCase__       )
										if text is not None:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.tokenizer(lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is None:
													return inputs

										elif audio is None:
													return encodings
										else:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					encodings['input_ids']
													return inputs






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__="np"       ):





										"""simple docstring"""




										return self.tokenizer.get_prompt_ids(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__       )





