




'''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							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       torch.FloatTensor
     SCREAMING_SNAKE_CASE_				:       torch.FloatTensor
     SCREAMING_SNAKE_CASE_				:       Optional[torch.FloatTensor]       							=		None







class   _a							(		__lowerCAmelCase   ,   __lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       Optional[int]       							=		2






     @register_to_config
     def __init__(					self   ,_SCREAMING_SNAKE_CASE = 0.0_2   ,_SCREAMING_SNAKE_CASE = 100   ,_SCREAMING_SNAKE_CASE = 1.0_0_7   ,_SCREAMING_SNAKE_CASE = 80   ,_SCREAMING_SNAKE_CASE = 0.0_5   ,_SCREAMING_SNAKE_CASE = 50   ,)				->     Any:
          # standard deviation of the initial noise distribution
          _snake_case				= sigma_max

          # setable values
          _snake_case				= None
          _snake_case				= None
          _snake_case				= None  # sigma(t_i)






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     torch.FloatTensor:
          return sample






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     Optional[Any]:
          _snake_case				= num_inference_steps
          _snake_case				= np.arange(0   ,self.num_inference_steps       )[::-1].copy()
          _snake_case				= torch.from_numpy(_SCREAMING_SNAKE_CASE       ).to(_SCREAMING_SNAKE_CASE       )
          _snake_case				= [
              (
                  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				= torch.tensor(_SCREAMING_SNAKE_CASE   ,dtype=torch.floataa   ,device=_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     Tuple[torch.FloatTensor, float]:
          if self.config.s_min <= sigma <= self.config.s_max:
               _snake_case				= min(self.config.s_churn / self.num_inference_steps   ,2**0.5 - 1       )
          else:
               _snake_case				= 0

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

          return sample_hat, sigma_hat






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = True   ,)				->     Union[KarrasVeOutput, Tuple]:
          _snake_case				= sample_hat + sigma_hat * model_output
          _snake_case				= (sample_hat - pred_original_sample) / sigma_hat
          _snake_case				= sample_hat + (sigma_prev - sigma_hat) * derivative

          if not return_dict:
               return (sample_prev, derivative)

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






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = True   ,)				->     Union[KarrasVeOutput, Tuple]:
          _snake_case				= sample_prev + sigma_prev * model_output
          _snake_case				= (sample_prev - pred_original_sample) / sigma_prev
          _snake_case				= 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=_SCREAMING_SNAKE_CASE   ,derivative=_SCREAMING_SNAKE_CASE   ,pred_original_sample=_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     Tuple:
          raise NotImplementedError()








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_		:     List[Any]          =		{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Optional[Any]          =		[
       '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GitForCausalLM''',
       '''GitModel''',
       '''GitPreTrainedModel''',
       '''GitVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
   from .processing_git import GitProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_git import (
          GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          GitForCausalLM,
          GitModel,
          GitPreTrainedModel,
          GitVisionModel,
      )

else:
   import sys

   UpperCamelCase_		:     List[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


