





'''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 lowerCamelCase						(__lowercase					):
		_lowercase  :	torch.FloatTensor
		_lowercase  :	torch.FloatTensor
		_lowercase  :	Optional[torch.FloatTensor]       					=				None





class lowerCamelCase						(__lowercase						,      __lowercase					):
		_lowercase  :	Any       					=				2







		@register_to_config
		def __init__(		self     ,     lowercase__ = 0.02     ,     lowercase__ = 100     ,     lowercase__ = 1.007     ,     lowercase__ = 80     ,     lowercase__ = 0.05     ,     lowercase__ = 50     ,     )		->						Optional[int]:






								"""simple docstring"""





								_snake_case		:	Tuple					     =     sigma_max

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







		def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						torch.FloatTensor:






								"""simple docstring"""





								return sample







		def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None		)		->						Any:






								"""simple docstring"""





								_snake_case		:	Optional[Any]					     =     num_inference_steps
								_snake_case		:	Union[str, Any]					     =     np.arange(0     ,     self.num_inference_steps		)[::-1].copy()
								_snake_case		:	Dict					     =     torch.from_numpy(__a		).to(__a		)
								_snake_case		:	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
								]
								_snake_case		:	List[Any]					     =     torch.tensor(__a     ,     dtype=torch.floataa     ,     device=__a		)







		def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None		)		->						Tuple[torch.FloatTensor, float]:






								"""simple docstring"""





								if self.config.s_min <= sigma <= self.config.s_max:
														_snake_case		:	str					     =     min(self.config.s_churn / self.num_inference_steps     ,     2**0.5 - 1		)
								else:
														_snake_case		:	Optional[Any]					     =     0

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

								return sample_hat, sigma_hat







		def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = True     ,     )		->						Union[KarrasVeOutput, Tuple]:






								"""simple docstring"""





								_snake_case		:	List[str]					     =     sample_hat + sigma_hat * model_output
								_snake_case		:	Tuple					     =     (sample_hat - pred_original_sample) / sigma_hat
								_snake_case		:	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 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = True     ,     )		->						Union[KarrasVeOutput, Tuple]:






								"""simple docstring"""





								_snake_case		:	List[str]					     =     sample_prev + sigma_prev * model_output
								_snake_case		:	List[str]					     =     (sample_prev - pred_original_sample) / sigma_prev
								_snake_case		:	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 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						str:






								"""simple docstring"""





								raise NotImplementedError()









'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		Dict		=							{'configuration_timm_backbone': ['TimmBackboneConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							['TimmBackbone']


if TYPE_CHECKING:
   from .configuration_timm_backbone import TimmBackboneConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_timm_backbone import TimmBackbone

else:
   import sys

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


