# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class _lowercase       (							_UpperCAmelCase    ):





							"""simple docstring"""




							lowerCAmelCase__							=       42
							lowerCAmelCase__							=       None






def        __lowerCAmelCase      (	_A			,_A=0.9_9_9			,_A="cosine"			,):



							"""simple docstring"""


							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_A       ):
																					return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_A       ):
																					return math.exp(t * -1_2.0       )

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

							_lowercase					=						[]
							for i in range(_A       ):
														_lowercase					=						i / num_diffusion_timesteps
														_lowercase					=						(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       (							_UpperCAmelCase ,					_UpperCAmelCase    ):





							"""simple docstring"""




							lowerCAmelCase__							=       1



							@register_to_config
							def __init__(     self  , UpperCAmelCase = 1000  , UpperCAmelCase = 0.0_001  , UpperCAmelCase = 0.02  , UpperCAmelCase = "linear"  , UpperCAmelCase = None  , UpperCAmelCase = True  , UpperCAmelCase = True  , UpperCAmelCase = 0  , UpperCAmelCase = "epsilon"  , UpperCAmelCase = 1.0  , **UpperCAmelCase  , ):







														'''simple docstring'''






														if kwargs.get("""set_alpha_to_one"""  , __lowerCAmelCase	) is not None:
																					_lowercase					=						(
																					    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
																					)
																					deprecate("""set_alpha_to_one"""  , """1.0.0"""  , __lowerCAmelCase  , standard_warn=__lowerCAmelCase	)
																					_lowercase					=						kwargs['''set_alpha_to_one''']
														if trained_betas is not None:
																					_lowercase					=						torch.tensor(__lowerCAmelCase  , dtype=torch.floataa	)
														elif beta_schedule == "linear":
																					_lowercase					=						torch.linspace(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , dtype=torch.floataa	)
														elif beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					_lowercase					=						(
																					    torch.linspace(beta_start**0.5  , beta_end**0.5  , __lowerCAmelCase  , dtype=torch.floataa	) ** 2
																					)
														elif beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					_lowercase					=						betas_for_alpha_bar(__lowerCAmelCase	)
														else:
																					raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

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

														# At every step in inverted ddim, we are looking into the next alphas_cumprod
														# For the final step, there is no next alphas_cumprod, and the index is out of bounds
														# `set_alpha_to_zero` decides whether we set this parameter simply to zero
														# in this case, self.step() just output the predicted noise
														# or whether we use the final alpha of the "non-previous" one.
														_lowercase					=						torch.tensor(0.0	) if set_alpha_to_zero else self.alphas_cumprod[-1]

														# standard deviation of the initial noise distribution
														_lowercase					=						1.0

														# setable values
														_lowercase					=						None
														_lowercase					=						torch.from_numpy(np.arange(0  , __lowerCAmelCase	).copy().astype(np.intaa	)	)



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







														'''simple docstring'''






														return sample



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







														'''simple docstring'''






														if num_inference_steps > self.config.num_train_timesteps:
																					raise ValueError(
																					    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
																					    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
																					    F''' maximal {self.config.num_train_timesteps} timesteps.'''	)

														_lowercase					=						num_inference_steps
														_lowercase					=						self.config.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
														_lowercase					=						(np.arange(0  , __lowerCAmelCase	) * step_ratio).round().copy().astype(np.intaa	)
														_lowercase					=						torch.from_numpy(__lowerCAmelCase	).to(__lowerCAmelCase	)
														self.timesteps += self.config.steps_offset



							def      _UpperCAmelCase							(     self  , UpperCAmelCase  , UpperCAmelCase  , UpperCAmelCase  , UpperCAmelCase = 0.0  , UpperCAmelCase = False  , UpperCAmelCase = None  , UpperCAmelCase = True  , ):







														'''simple docstring'''






														_lowercase					=						timestep + self.config.num_train_timesteps // self.num_inference_steps

														# 2. compute alphas, betas
														# change original implementation to exactly match noise levels for analogous forward process
														_lowercase					=						self.alphas_cumprod[timestep]
														_lowercase					=						(
														    self.alphas_cumprod[prev_timestep]
														    if prev_timestep < self.config.num_train_timesteps
														    else self.final_alpha_cumprod
														)

														_lowercase					=						1 - alpha_prod_t

														# 3. compute predicted original sample from predicted noise also called
														# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
														if self.config.prediction_type == "epsilon":
																					_lowercase					=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																					_lowercase					=						model_output
														elif self.config.prediction_type == "sample":
																					_lowercase					=						model_output
																					_lowercase					=						(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
														elif self.config.prediction_type == "v_prediction":
																					_lowercase					=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																					_lowercase					=						(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
														else:
																					raise ValueError(
																					    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
																					    """ `v_prediction`"""	)

														# 4. Clip or threshold "predicted x_0"
														if self.config.clip_sample:
																					_lowercase					=						pred_original_sample.clamp(
																					    -self.config.clip_sample_range  , self.config.clip_sample_range	)

														# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
														_lowercase					=						(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

														# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
														_lowercase					=						alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

														if not return_dict:
																					return (prev_sample, pred_original_sample)
														return DDIMSchedulerOutput(prev_sample=__lowerCAmelCase  , pred_original_sample=__lowerCAmelCase	)



							def __len__(     self	):







														'''simple docstring'''






														return self.config.num_train_timesteps










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE__   :      Optional[int]				=		{"""tokenization_herbert""": ["""HerbertTokenizer"""]}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		["""HerbertTokenizerFast"""]


if TYPE_CHECKING:
					from .tokenization_herbert import HerbertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_herbert_fast import HerbertTokenizerFast

else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






