



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 		snake_case						(	lowercase_  ):



   '''simple docstring'''






   snake_case_					: torch.FloatTensor
   snake_case_					: torch.FloatTensor
   snake_case_					: Optional[torch.FloatTensor]          =		None







class 		snake_case						(	lowercase_					,lowercase_  ):



   '''simple docstring'''






   snake_case_					: List[Any]          =		2

   @register_to_config
   def __init__(					self							:			Any				,		lowerCAmelCase							:			float = 0.02				,		lowerCAmelCase							:			float = 100				,		lowerCAmelCase							:			float = 1.007				,		lowerCAmelCase							:			float = 80				,		lowerCAmelCase							:			float = 0.05				,		lowerCAmelCase							:			float = 50				,		)				->				str:

    """simple docstring"""







    _snake_case    :  List[str]					=					sigma_max

    # setable values
    _snake_case    :  str					=					None
    _snake_case    :  Dict					=					None
    _snake_case    :  int					=					None  # sigma(t_i)

   def      UpperCamelCase_  (					self							:			Tuple				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			Optional[int] = None)				->				Optional[int]:

    """simple docstring"""







    return sample

   def      UpperCamelCase_  (					self							:			int				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			Union[str, torch.device] = None)				->				Any:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			Optional[torch.Generator] = None)				->				Any:

    """simple docstring"""







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

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

    return sample_hat, sigma_hat

   def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			bool = True				,		)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  Tuple					=					sample_hat + sigma_hat * model_output
    _snake_case    :  List[Any]					=					(sample_hat - pred_original_sample) / sigma_hat
    _snake_case    :  List[Any]					=					sample_hat + (sigma_prev - sigma_hat) * derivative

    if not return_dict:
     return (sample_prev, derivative)

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

   def      UpperCamelCase_  (					self							:			Union[str, Any]				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			float				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			torch.FloatTensor				,		lowerCAmelCase							:			bool = True				,		)				->				Any:

    """simple docstring"""







    _snake_case    :  int					=					sample_prev + sigma_prev * model_output
    _snake_case    :  int					=					(sample_prev - pred_original_sample) / sigma_prev
    _snake_case    :  List[str]					=					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=UpperCamelCase__				,		derivative=UpperCamelCase__				,		pred_original_sample=UpperCamelCase__)


   def      UpperCamelCase_  (					self							:			Any				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Optional[int])				->				Tuple:

    """simple docstring"""







    raise NotImplementedError()





from typing import TYPE_CHECKING

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


a__    =	{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CanineForMultipleChoice""",
      """CanineForQuestionAnswering""",
      """CanineForSequenceClassification""",
      """CanineForTokenClassification""",
      """CanineLayer""",
      """CanineModel""",
      """CaninePreTrainedModel""",
      """load_tf_weights_in_canine""",
  ]


if TYPE_CHECKING:
  from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
  from .tokenization_canine import CanineTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_canine import (
        CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
        CanineForMultipleChoice,
        CanineForQuestionAnswering,
        CanineForSequenceClassification,
        CanineForTokenClassification,
        CanineLayer,
        CanineModel,
        CaninePreTrainedModel,
        load_tf_weights_in_canine,
    )


else:
  import sys

  a__    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
