


"""simple docstring"""


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 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					torch.FloatTensor
     snake_case__	:					torch.FloatTensor
     snake_case__	:					Optional[torch.FloatTensor]													=None


class 			A__	(						__lowercase ,     __lowercase):


     """simple docstring"""
     snake_case__	:					Any													=2

     @register_to_config
     def __init__(	self:						Any     ,		__a:						float = 0.02     ,		__a:						float = 100     ,		__a:						float = 1.0_07     ,		__a:						float = 80     ,		__a:						float = 0.05     ,		__a:						float = 50     ,		)->    Optional[int]:
           # standard deviation of the initial noise distribution
           lowerCamelCase					:					Tuple        =	sigma_max

           # setable values
           lowerCamelCase					:					int        =	None
           lowerCamelCase					:					np.IntTensor        =	None
           lowerCamelCase					:					torch.FloatTensor        =	None  # sigma(t_i)

     def 		a__						(	self:						List[str]     ,		__a:						torch.FloatTensor     ,		__a:						Optional[int] = None	)->    torch.FloatTensor:
           return sample

     def 		a__						(	self:						Tuple     ,		__a:						int     ,		__a:						Union[str, torch.device] = None	)->    Any:
           lowerCamelCase					:					Optional[Any]        =	num_inference_steps
           lowerCamelCase					:					Union[str, Any]        =	np.arange(0     ,		self.num_inference_steps	)[::-1].copy()
           lowerCamelCase					:					Dict        =	torch.from_numpy(__a	).to(__a	)
           lowerCamelCase					:					int        =	[
               (
                   self.config.sigma_max**2
                   * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
               )
               for i in self.timesteps
           ]
           lowerCamelCase					:					List[Any]        =	torch.tensor(__a     ,		dtype=torch.floataa     ,		device=__a	)

     def 		a__						(	self:						int     ,		__a:						torch.FloatTensor     ,		__a:						float     ,		__a:						Optional[torch.Generator] = None	)->    Tuple[torch.FloatTensor, float]:
           if self.config.s_min <= sigma <= self.config.s_max:
                 lowerCamelCase					:					str        =	min(self.config.s_churn / self.num_inference_steps     ,		2**0.5 - 1	)
           else:
                 lowerCamelCase					:					Optional[Any]        =	0

           # sample eps ~ N(0, S_noise^2 * I)
           lowerCamelCase					:					str        =	self.config.s_noise * randn_tensor(sample.shape     ,		generator=__a	).to(sample.device	)
           lowerCamelCase					:					str        =	sigma + gamma * sigma
           lowerCamelCase					:					Dict        =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

           return sample_hat, sigma_hat

     def 		a__						(	self:						Optional[Any]     ,		__a:						torch.FloatTensor     ,		__a:						float     ,		__a:						float     ,		__a:						torch.FloatTensor     ,		__a:						bool = True     ,		)->    Union[KarrasVeOutput, Tuple]:
           lowerCamelCase					:					List[str]        =	sample_hat + sigma_hat * model_output
           lowerCamelCase					:					Tuple        =	(sample_hat - pred_original_sample) / sigma_hat
           lowerCamelCase					:					Optional[Any]        =	sample_hat + (sigma_prev - sigma_hat) * derivative

           if not return_dict:
                 return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=__a     ,		derivative=__a     ,		pred_original_sample=__a	)

     def 		a__						(	self:						Dict     ,		__a:						torch.FloatTensor     ,		__a:						float     ,		__a:						float     ,		__a:						torch.FloatTensor     ,		__a:						torch.FloatTensor     ,		__a:						torch.FloatTensor     ,		__a:						bool = True     ,		)->    Union[KarrasVeOutput, Tuple]:
           lowerCamelCase					:					List[str]        =	sample_prev + sigma_prev * model_output
           lowerCamelCase					:					List[str]        =	(sample_prev - pred_original_sample) / sigma_prev
           lowerCamelCase					:					Any        =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

           if not return_dict:
                 return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=__a     ,		derivative=__a     ,		pred_original_sample=__a	)







     def 		a__						(	self:						Dict     ,		__a:						Dict     ,		__a:						Dict     ,		__a:						Union[str, Any]	)->    str:
           raise NotImplementedError()








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCamelCase      :Optional[Any]	    =		{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Union[str, Any]	    =		[
        'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
        'EncodecModel',
        'EncodecPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_encodec import (
        ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
        EncodecConfig,
    )
    from .feature_extraction_encodec import EncodecFeatureExtractor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encodec import (
            ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
            EncodecModel,
            EncodecPreTrainedModel,
        )

else:
    import sys

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




