





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     __magic_name__     (							__SCREAMING_SNAKE_CASE							):





     """simple docstring"""



     __UpperCamelCase    			=   42
     __UpperCamelCase    			=   42




class     __magic_name__     (							__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE							):





     """simple docstring"""



     __UpperCamelCase    			=   1



     @register_to_config
     def __init__(							self				:Any	,					snake_case				:Optional[Any] = 2_000	,					snake_case				:Union[str, Any] = 0.15	,					snake_case				:Optional[int] = 0.01	,					snake_case				:str = 1348.0	,					snake_case				:List[str] = 1e-5	,					snake_case				:List[Any] = 1	,					):





        '''simple docstring'''




        A_    : Tuple								=	sigma_max

        # setable values
        A_    : str								=	None

        self.set_sigmas(_a	,					_a	,					_a	,					_a     )



     def   SCREAMING_SNAKE_CASE						(							self				:Tuple	,					snake_case				:List[str]	,					snake_case				:Dict = None     ):





        '''simple docstring'''




        return sample



     def   SCREAMING_SNAKE_CASE						(							self				:Any	,					snake_case				:str	,					snake_case				:Any = None	,					snake_case				:Optional[Any] = None     ):





        '''simple docstring'''




        A_    : Optional[int]								=	sampling_eps if sampling_eps is not None else self.config.sampling_eps

        A_    : Optional[int]								=	torch.linspace(1	,					_a	,					_a	,					device=_a     )



     def   SCREAMING_SNAKE_CASE						(							self				:Optional[Any]	,					snake_case				:Tuple	,					snake_case				:str = None	,					snake_case				:Dict = None	,					snake_case				:str = None     ):





        '''simple docstring'''




        A_    : List[str]								=	sigma_min if sigma_min is not None else self.config.sigma_min
        A_    : List[str]								=	sigma_max if sigma_max is not None else self.config.sigma_max
        A_    : Any								=	sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
           self.set_timesteps(_a	,					_a     )

        A_    : Optional[int]								=	sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        A_    : Optional[int]								=	torch.exp(torch.linspace(math.log(_a     )	,					math.log(_a     )	,					_a     )     )
        A_    : List[Any]								=	torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )



     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					snake_case				:Tuple	,					snake_case				:Dict     ):





        '''simple docstring'''




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



     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					snake_case				:Any	,					snake_case				:str	,					snake_case				:Dict	,					snake_case				:Dict = None	,					snake_case				:List[Any] = True	,					):





        '''simple docstring'''




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

        A_    : List[str]								=	timestep * torch.ones(
            sample.shape[0]	,					device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
        A_    : str								=	(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
        A_    : List[Any]								=	timesteps.to(self.discrete_sigmas.device     )

        A_    : str								=	self.discrete_sigmas[timesteps].to(sample.device     )
        A_    : List[Any]								=	self.get_adjacent_sigma(_a	,					_a     ).to(sample.device     )
        A_    : Union[str, Any]								=	torch.zeros_like(_a     )
        A_    : Dict								=	(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
        A_    : List[Any]								=	diffusion.flatten()
        while len(diffusion.shape     ) < len(sample.shape     ):
           A_    : List[Any]								=	diffusion.unsqueeze(-1     )
        A_    : Optional[Any]								=	drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        A_    : Any								=	randn_tensor(
            sample.shape	,					layout=sample.layout	,					generator=_a	,					device=sample.device	,					dtype=sample.dtype     )
        A_    : Optional[Any]								=	sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        A_    : Tuple								=	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=_a	,					prev_sample_mean=_a     )



     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					snake_case				:Union[str, Any]	,					snake_case				:List[str]	,					snake_case				:List[str] = None	,					snake_case				:Dict = True	,					):





        '''simple docstring'''




        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
        A_    : Tuple								=	randn_tensor(sample.shape	,					layout=sample.layout	,					generator=_a     ).to(sample.device     )

        # compute step size from the model_output, the noise, and the snr
        A_    : Tuple								=	torch.norm(model_output.reshape(model_output.shape[0]	,					-1     )	,					dim=-1     ).mean()
        A_    : Tuple								=	torch.norm(noise.reshape(noise.shape[0]	,					-1     )	,					dim=-1     ).mean()
        A_    : int								=	(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        A_    : Dict								=	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
        A_    : Optional[Any]								=	step_size.flatten()
        while len(step_size.shape     ) < len(sample.shape     ):
           A_    : int								=	step_size.unsqueeze(-1     )
        A_    : List[Any]								=	sample + step_size * model_output
        A_    : Tuple								=	prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
           return (prev_sample,)

        return SchedulerOutput(prev_sample=_a     )



     def   SCREAMING_SNAKE_CASE						(							self				:Tuple	,					snake_case				:Union[str, Any]	,					snake_case				:Union[str, Any]	,					snake_case				:Optional[Any]	,					):





        '''simple docstring'''




        A_    : Tuple								=	timesteps.to(original_samples.device     )
        A_    : Optional[int]								=	self.discrete_sigmas.to(original_samples.device     )[timesteps]
        A_    : Optional[int]								=	(
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(_a     ) * sigmas[:, None, None, None]
        )
        A_    : int								=	noise + original_samples
        return noisy_samples





     def __len__(							self				:Tuple     ):





        '''simple docstring'''




        return self.config.num_train_timesteps




"""simple docstring"""




lowercase_			     =  [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_			     =  [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_			     =  {
    0: "Sunday",
    1: "Monday",
    2: "Tuesday",
    3: "Wednesday",
    4: "Thursday",
    5: "Friday",
    6: "Saturday",
}

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int			) ->      str:
		assert len(str(lowerCAmelCase__			)			) > 2, "year should be in YYYY format"
		assert 1 <= month <= 12, "month should be between 1 to 12"
		assert 1 <= day <= 31, "day should be between 1 to 31"

		# Doomsday algorithm:
		__a           =			year // 100
		__a           =			(5 * (century % 4) + 2) % 7
		__a           =			year % 100
		__a           =			centurian % 12
		__a           =			(
		    (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
		) % 7
		__a           =			(
		    DOOMSDAY_NOT_LEAP[month - 1]
		    if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
		    else DOOMSDAY_LEAP[month - 1]
		)
		__a           =			(dooms_day + day - day_anchor) % 7
		return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
				import doctest

				doctest.testmod()






