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

import numpy as np
import torch

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





@dataclass
class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				torch.FloatTensor
							lowerCamelCase__:				torch.FloatTensor



def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Tuple=0.999				,					snake_case_							:		Union[str, Any]="cosine"				,					):



	'''simple docstring'''



	if alpha_transform_type == "cosine":

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

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(snake_case_							:		List[str]				):
			return math.exp(t * -12.0				)

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

	UpperCAmelCase:					Union[str, Any] 						=     []
	for i in range(snake_case_				):
		UpperCAmelCase:					List[str] 						=     i / num_diffusion_timesteps
		UpperCAmelCase:					List[str] 						=     (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 		__lowerCamelCase     (   lowercase							, lowercase		):
							lowerCamelCase__:				Any      =							1



							@register_to_config
							def __init__(     self	,				__snake_case = 1_0_0_0	,				__snake_case = 0.00_01	,				__snake_case = 0.02	,				__snake_case = "linear"	,				__snake_case = 0.0	,				__snake_case = None	,				__snake_case = True	,				)						->     List[str]:






								"""simple docstring"""



								if trained_betas is not None:
									UpperCAmelCase:					Union[str, Any] 						=     torch.from_numpy(__snake_case							)
								elif beta_schedule == "linear":
									UpperCAmelCase:					List[str] 						=     torch.linspace(__snake_case	,				__snake_case	,				__snake_case	,				dtype=torch.floataa							)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									UpperCAmelCase:					int 						=     (
									    torch.linspace(beta_start**0.5	,				beta_end**0.5	,				__snake_case	,				dtype=torch.floataa							) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									UpperCAmelCase:					Any 						=     betas_for_alpha_bar(__snake_case							)
								elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									UpperCAmelCase:					List[str] 						=     torch.linspace(-6	,				6	,				__snake_case							)
									UpperCAmelCase:					List[Any] 						=     torch.sigmoid(__snake_case							) * (beta_end - beta_start) + beta_start
								else:
									raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'							)

								UpperCAmelCase:					Optional[int] 						=     1.0 - self.betas
								UpperCAmelCase:					Optional[Any] 						=     torch.cumprod(self.alphas	,				dim=0							)
								UpperCAmelCase:					Optional[Any] 						=     torch.tensor(1.0							)

								UpperCAmelCase:					Optional[Any] 						=     torch.tensor(1.0							)

								# standard deviation of the initial noise distribution
								UpperCAmelCase:					str 						=     1.0

								# setable values
								UpperCAmelCase:					int 						=     None
								UpperCAmelCase:					int 						=     torch.from_numpy(np.arange(0	,				__snake_case							)[::-1].copy()							)

								UpperCAmelCase:					Optional[Any] 						=     eta



							def     A__ (     self	,				__snake_case	,				__snake_case = None							)						->     torch.FloatTensor:






								"""simple docstring"""



								return sample



							def     A__ (     self	,				__snake_case	,				__snake_case = 1_0	,				__snake_case = 1_0	,				__snake_case = None	,				)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     min(self.config.num_train_timesteps	,				__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     num_inference_steps

								UpperCAmelCase:					Dict 						=     []

								UpperCAmelCase:					Optional[Any] 						=     {}
								for j in range(0	,				num_inference_steps - jump_length	,				__snake_case							):
									UpperCAmelCase:					int 						=     jump_n_sample - 1

								UpperCAmelCase:					Tuple 						=     num_inference_steps
								while t >= 1:
									UpperCAmelCase:					List[str] 						=     t - 1
									timesteps.append(__snake_case							)

									if jumps.get(__snake_case	,				0							) > 0:
										UpperCAmelCase:					Dict 						=     jumps[t] - 1
										for _ in range(__snake_case							):
											UpperCAmelCase:					Optional[Any] 						=     t + 1
											timesteps.append(__snake_case							)

								UpperCAmelCase:					Optional[int] 						=     np.array(__snake_case							) * (self.config.num_train_timesteps // self.num_inference_steps)
								UpperCAmelCase:					Union[str, Any] 						=     torch.from_numpy(__snake_case							).to(__snake_case							)



							def     A__ (     self	,				__snake_case							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     t - self.config.num_train_timesteps // self.num_inference_steps

								UpperCAmelCase:					List[str] 						=     self.alphas_cumprod[t]
								UpperCAmelCase:					Tuple 						=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								UpperCAmelCase:					List[Any] 						=     1 - alpha_prod_t
								UpperCAmelCase:					List[Any] 						=     1 - alpha_prod_t_prev

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from
								# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
								# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
								# variance to pred_sample
								# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
								# without eta.
								# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
								UpperCAmelCase:					int 						=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case	,				__snake_case = None	,				__snake_case = True	,				)						->     Union[RePaintSchedulerOutput, Tuple]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     timestep
								UpperCAmelCase:					Union[str, Any] 						=     timestep - self.config.num_train_timesteps // self.num_inference_steps

								# 1. compute alphas, betas
								UpperCAmelCase:					Optional[Any] 						=     self.alphas_cumprod[t]
								UpperCAmelCase:					Dict 						=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
								UpperCAmelCase:					Any 						=     1 - alpha_prod_t

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								UpperCAmelCase:					str 						=     (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
									UpperCAmelCase:					Any 						=     torch.clamp(__snake_case	,				-1	,				1							)

								# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
								# substitute formula (7) in the algorithm coming from DDPM paper
								# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
								# DDIM schedule gives the same results as DDPM with eta = 1.0
								# Noise is being reused in 7. and 8., but no impact on quality has
								# been observed.

								# 5. Add noise
								UpperCAmelCase:					Union[str, Any] 						=     model_output.device
								UpperCAmelCase:					Optional[int] 						=     randn_tensor(model_output.shape	,				generator=__snake_case	,				device=__snake_case	,				dtype=model_output.dtype							)
								UpperCAmelCase:					Any 						=     self.eta * self._get_variance(__snake_case							) ** 0.5

								UpperCAmelCase:					List[str] 						=     0
								if t > 0 and self.eta > 0:
									UpperCAmelCase:					List[str] 						=     std_dev_t * noise

								# 6. compute "direction pointing to x_t" of formula (12)
								# from https://arxiv.org/pdf/2010.02502.pdf
								UpperCAmelCase:					int 						=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

								# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								UpperCAmelCase:					Dict 						=     alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

								# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
								UpperCAmelCase:					int 						=     (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

								# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
								UpperCAmelCase:					Tuple 						=     mask * prev_known_part + (1.0 - mask) * prev_unknown_part

								if not return_dict:
									return (
									    pred_prev_sample,
									    pred_original_sample,
									)

								return RePaintSchedulerOutput(prev_sample=__snake_case	,				pred_original_sample=__snake_case							)



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case=None							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     self.config.num_train_timesteps // self.num_inference_steps

								for i in range(__snake_case							):
									UpperCAmelCase:					Any 						=     self.betas[timestep + i]
									if sample.device.type == "mps":
										# randn does not work reproducibly on mps
										UpperCAmelCase:					Optional[int] 						=     randn_tensor(sample.shape	,				dtype=sample.dtype	,				generator=__snake_case							)
										UpperCAmelCase:					int 						=     noise.to(sample.device							)
									else:
										UpperCAmelCase:					List[Any] 						=     randn_tensor(sample.shape	,				generator=__snake_case	,				device=sample.device	,				dtype=sample.dtype							)

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									UpperCAmelCase:					Any 						=     (1 - beta) ** 0.5 * sample + beta**0.5 * noise

								return sample



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case	,				)						->     torch.FloatTensor:






								"""simple docstring"""



								raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."							)




							def __len__(     self							)						->     str:






								"""simple docstring"""



								return self.config.num_train_timesteps


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Dict				      =     ['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
