


'''simple docstring'''






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 					_snake_case   (     A ,    A=0.999 ,    A="cosine" ,    )						->    Optional[int]:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(A       ):
																					return math.cos((t + 0.008) / 1.008 * 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}"""       )

							lowerCAmelCase__          =	[]
							for i in range(A       ):
														lowerCAmelCase__          =	i / num_diffusion_timesteps
														lowerCAmelCase__          =	(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 		a__				(							a__				,				a__  ):





							'''simple docstring'''






							lowercase__			:   List[Any]							=      [e.name for e in KarrasDiffusionSchedulers]
							lowercase__			:   Optional[Any]							=      2







							@register_to_config
							def __init__(		self					,    lowerCamelCase_ = 10_00					,    lowerCamelCase_ = 0.00_085					,    lowerCamelCase_ = 0.012					,    lowerCamelCase_ = "linear"					,    lowerCamelCase_ = None					,    lowerCamelCase_ = "epsilon"					,    lowerCamelCase_ = "linspace"					,    lowerCamelCase_ = 0					,    )       ->							Tuple:
														if trained_betas is not None:
																					lowerCAmelCase__          =	torch.tensor(lowerCamelCase_					,    dtype=torch.floataa   )
														elif beta_schedule == "linear":
																					lowerCAmelCase__          =	torch.linspace(lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    dtype=torch.floataa   )
														elif beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					lowerCAmelCase__          =	(
																					    torch.linspace(beta_start**0.5					,    beta_end**0.5					,    lowerCamelCase_					,    dtype=torch.floataa   ) ** 2
																					)
														elif beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					lowerCAmelCase__          =	betas_for_alpha_bar(lowerCamelCase_   )
														else:
																					raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""   )

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

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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_=None   )       ->							Any:
														if schedule_timesteps is None:
																					lowerCAmelCase__          =	self.timesteps

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

														return indices[pos].item()







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Dict:
														# standard deviation of the initial noise distribution
														if self.config.timestep_spacing in ["linspace", "trailing"]:
																					return self.sigmas.max()

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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    )       ->							torch.FloatTensor:
														lowerCAmelCase__          =	self.index_for_timestep(lowerCamelCase_   )

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

														lowerCAmelCase__          =	sample / ((sigma**2 + 1) ** 0.5)
														return sample







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_ = None					,    lowerCamelCase_ = None					,    )       ->							List[str]:
														lowerCAmelCase__          =	num_inference_steps

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

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

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

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

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

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

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

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

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

														lowerCAmelCase__          =	None

														# for exp beta schedules, such as the one for `pipeline_shap_e.py`
														# we need an index counter
														lowerCAmelCase__          =	defaultdict(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							List[str]:
														# get log sigma
														lowerCAmelCase__          =	sigma.log()

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

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

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

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

														# transform interpolation to time range
														lowerCAmelCase__          =	(1 - w) * low_idx + w * high_idx
														lowerCAmelCase__          =	t.view(sigma.shape   )
														return t







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							str:
														return self.sample is None







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_ = None					,    lowerCamelCase_ = True					,    )       ->							Union[SchedulerOutput, Tuple]:
														lowerCAmelCase__          =	self.index_for_timestep(lowerCamelCase_   )

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

														if self.state_in_first_order:
																					lowerCAmelCase__          =	self.sigmas[step_index]
																					lowerCAmelCase__          =	self.sigmas_interpol[step_index]
																					lowerCAmelCase__          =	self.sigmas_up[step_index]
																					lowerCAmelCase__          =	self.sigmas_down[step_index - 1]
														else:
																					# 2nd order / KPDM2's method
																					lowerCAmelCase__          =	self.sigmas[step_index - 1]
																					lowerCAmelCase__          =	self.sigmas_interpol[step_index - 1]
																					lowerCAmelCase__          =	self.sigmas_up[step_index - 1]
																					lowerCAmelCase__          =	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
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

														lowerCAmelCase__          =	model_output.device
														lowerCAmelCase__          =	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":
																					lowerCAmelCase__          =	sigma_hat if self.state_in_first_order else sigma_interpol
																					lowerCAmelCase__          =	sample - sigma_input * model_output
														elif self.config.prediction_type == "v_prediction":
																					lowerCAmelCase__          =	sigma_hat if self.state_in_first_order else sigma_interpol
																					lowerCAmelCase__          =	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
																					lowerCAmelCase__          =	(sample - pred_original_sample) / sigma_hat
																					# 3. delta timestep
																					lowerCAmelCase__          =	sigma_interpol - sigma_hat

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

																					lowerCAmelCase__          =	self.sample
																					lowerCAmelCase__          =	None

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

														if not return_dict:
																					return (prev_sample,)

														return SchedulerOutput(prev_sample=lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    )       ->							torch.FloatTensor:
														# Make sure sigmas and timesteps have the same device and dtype as original_samples
														lowerCAmelCase__          =	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
																					lowerCAmelCase__          =	self.timesteps.to(original_samples.device					,    dtype=torch.floataa   )
																					lowerCAmelCase__          =	timesteps.to(original_samples.device					,    dtype=torch.floataa   )
														else:
																					lowerCAmelCase__          =	self.timesteps.to(original_samples.device   )
																					lowerCAmelCase__          =	timesteps.to(original_samples.device   )

														lowerCAmelCase__          =	[self.index_for_timestep(lowerCamelCase_					,    lowerCamelCase_   ) for t in timesteps]

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

														lowerCAmelCase__          =	original_samples + noise * sigma
														return noisy_samples







							def __len__(		self   )       ->							List[str]:
														return self.config.num_train_timesteps



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=							{
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							[
		    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SwitchTransformersEncoderModel''',
		    '''SwitchTransformersForConditionalGeneration''',
		    '''SwitchTransformersModel''',
		    '''SwitchTransformersPreTrainedModel''',
		    '''SwitchTransformersTop1Router''',
		    '''SwitchTransformersSparseMLP''',
		]


if TYPE_CHECKING:
		from .configuration_switch_transformers import (
		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SwitchTransformersConfig,
		    SwitchTransformersOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwitchTransformersEncoderModel,
				    SwitchTransformersForConditionalGeneration,
				    SwitchTransformersModel,
				    SwitchTransformersPreTrainedModel,
				    SwitchTransformersSparseMLP,
				    SwitchTransformersTopaRouter,
				)


else:
		import sys

		__UpperCAmelCase    				=							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)