



import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput



@dataclass
class 	lowerCamelCase      (						lowerCAmelCase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    torch.FloatTensor
					lowerCAmelCase_				:    torch.FloatTensor





class 	lowerCamelCase      (						lowerCAmelCase__,     lowerCAmelCase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Any									=  1



					@register_to_config
					def __init__(			self    ,	lowerCAmelCase = 2000    ,	lowerCAmelCase = 0.15    ,	lowerCAmelCase = 0.01    ,	lowerCAmelCase = 1348.0    ,	lowerCAmelCase = 1e-5    ,	lowerCAmelCase = 1    ,	):
							UpperCAmelCase_						=				sigma_max

							# setable values
							UpperCAmelCase_						=				None

							self.set_sigmas(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							return sample



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

							UpperCAmelCase_						=				torch.linspace(1    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	device=_lowerCamelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				sigma_min if sigma_min is not None else self.config.sigma_min
							UpperCAmelCase_						=				sigma_max if sigma_max is not None else self.config.sigma_max
							UpperCAmelCase_						=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
									self.set_timesteps(_lowerCamelCase    ,	_lowerCamelCase     )

							UpperCAmelCase_						=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
							UpperCAmelCase_						=				torch.exp(torch.linspace(math.log(_lowerCamelCase     )    ,	math.log(_lowerCamelCase     )    ,	_lowerCamelCase     )     )
							UpperCAmelCase_						=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							return torch.where(
							    timesteps == 0    ,	torch.zeros_like(t.to(timesteps.device     )     )    ,	self.discrete_sigmas[timesteps - 1].to(timesteps.device     )    ,	)



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = True    ,	):





							if self.timesteps is None:
									raise ValueError(
									    "`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"     )

							UpperCAmelCase_						=				timestep * torch.ones(
							    sample.shape[0]    ,	device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
							UpperCAmelCase_						=				(timestep * (len(self.timesteps     ) - 1)).long()

							# mps requires indices to be in the same device, so we use cpu as is the default with cuda
							UpperCAmelCase_						=				timesteps.to(self.discrete_sigmas.device     )

							UpperCAmelCase_						=				self.discrete_sigmas[timesteps].to(sample.device     )
							UpperCAmelCase_						=				self.get_adjacent_sigma(_lowerCamelCase    ,	_lowerCamelCase     ).to(sample.device     )
							UpperCAmelCase_						=				torch.zeros_like(_lowerCamelCase     )
							UpperCAmelCase_						=				(sigma**2 - adjacent_sigma**2) ** 0.5

							# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
							# also equation 47 shows the analog from SDE models to ancestral sampling methods
							UpperCAmelCase_						=				diffusion.flatten()
							while len(diffusion.shape     ) < len(sample.shape     ):
									UpperCAmelCase_						=				diffusion.unsqueeze(-1     )
							UpperCAmelCase_						=				drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							UpperCAmelCase_						=				randn_tensor(
							    sample.shape    ,	layout=sample.layout    ,	generator=_lowerCamelCase    ,	device=sample.device    ,	dtype=sample.dtype     )
							UpperCAmelCase_						=				sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							UpperCAmelCase_						=				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

							if not return_dict:
									return (prev_sample, prev_sample_mean)

							return SdeVeOutput(prev_sample=_lowerCamelCase    ,	prev_sample_mean=_lowerCamelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase = True    ,	):





							if self.timesteps is None:
									raise ValueError(
									    "`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"     )

							# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
							# sample noise for correction
							UpperCAmelCase_						=				randn_tensor(sample.shape    ,	layout=sample.layout    ,	generator=_lowerCamelCase     ).to(sample.device     )

							# compute step size from the model_output, the noise, and the snr
							UpperCAmelCase_						=				torch.norm(model_output.reshape(model_output.shape[0]    ,	-1     )    ,	dim=-1     ).mean()
							UpperCAmelCase_						=				torch.norm(noise.reshape(noise.shape[0]    ,	-1     )    ,	dim=-1     ).mean()
							UpperCAmelCase_						=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
							UpperCAmelCase_						=				step_size * torch.ones(sample.shape[0]     ).to(sample.device     )
							# self.repeat_scalar(step_size, sample.shape[0])

							# compute corrected sample: model_output term and noise term
							UpperCAmelCase_						=				step_size.flatten()
							while len(step_size.shape     ) < len(sample.shape     ):
									UpperCAmelCase_						=				step_size.unsqueeze(-1     )
							UpperCAmelCase_						=				sample + step_size * model_output
							UpperCAmelCase_						=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
									return (prev_sample,)

							return SchedulerOutput(prev_sample=_lowerCamelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	):
							UpperCAmelCase_						=				timesteps.to(original_samples.device     )
							UpperCAmelCase_						=				self.discrete_sigmas.to(original_samples.device     )[timesteps]
							UpperCAmelCase_						=				(
							    noise * sigmas[:, None, None, None]
							    if noise is not None
							    else torch.randn_like(_lowerCamelCase     ) * sigmas[:, None, None, None]
							)
							UpperCAmelCase_						=				noise + original_samples
							return noisy_samples



					def __len__(			self     ):
							return self.config.num_train_timesteps






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE     				=						{"tokenization_herbert": ["HerbertTokenizer"]}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["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     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

