



"""simple docstring"""




from typing import TYPE_CHECKING

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


_UpperCamelCase:				Tuple       							=  {
    """auto_factory""": ["""get_values"""],
    """configuration_auto""": ["""ALL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CONFIG_MAPPING""", """MODEL_NAMES_MAPPING""", """AutoConfig"""],
    """feature_extraction_auto""": ["""FEATURE_EXTRACTOR_MAPPING""", """AutoFeatureExtractor"""],
    """image_processing_auto""": ["""IMAGE_PROCESSOR_MAPPING""", """AutoImageProcessor"""],
    """processing_auto""": ["""PROCESSOR_MAPPING""", """AutoProcessor"""],
    """tokenization_auto""": ["""TOKENIZER_MAPPING""", """AutoTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:				List[Any]       							=  [
         """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
         """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING""",
         """MODEL_FOR_AUDIO_XVECTOR_MAPPING""",
         """MODEL_FOR_BACKBONE_MAPPING""",
         """MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING""",
         """MODEL_FOR_CAUSAL_LM_MAPPING""",
         """MODEL_FOR_CTC_MAPPING""",
         """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING""",
         """MODEL_FOR_DEPTH_ESTIMATION_MAPPING""",
         """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
         """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING""",
         """MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING""",
         """MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING""",
         """MODEL_FOR_MASKED_LM_MAPPING""",
         """MODEL_FOR_MASK_GENERATION_MAPPING""",
         """MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
         """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
         """MODEL_FOR_OBJECT_DETECTION_MAPPING""",
         """MODEL_FOR_PRETRAINING_MAPPING""",
         """MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
         """MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING""",
         """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
         """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
         """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
         """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING""",
         """MODEL_FOR_TEXT_ENCODING_MAPPING""",
         """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
         """MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING""",
         """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING""",
         """MODEL_FOR_VISION_2_SEQ_MAPPING""",
         """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING""",
         """MODEL_MAPPING""",
         """MODEL_WITH_LM_HEAD_MAPPING""",
         """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING""",
         """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING""",
         """AutoModel""",
         """AutoBackbone""",
         """AutoModelForAudioClassification""",
         """AutoModelForAudioFrameClassification""",
         """AutoModelForAudioXVector""",
         """AutoModelForCausalLM""",
         """AutoModelForCTC""",
         """AutoModelForDepthEstimation""",
         """AutoModelForImageClassification""",
         """AutoModelForImageSegmentation""",
         """AutoModelForInstanceSegmentation""",
         """AutoModelForMaskGeneration""",
         """AutoModelForTextEncoding""",
         """AutoModelForMaskedImageModeling""",
         """AutoModelForMaskedLM""",
         """AutoModelForMultipleChoice""",
         """AutoModelForNextSentencePrediction""",
         """AutoModelForObjectDetection""",
         """AutoModelForPreTraining""",
         """AutoModelForQuestionAnswering""",
         """AutoModelForSemanticSegmentation""",
         """AutoModelForSeq2SeqLM""",
         """AutoModelForSequenceClassification""",
         """AutoModelForSpeechSeq2Seq""",
         """AutoModelForTableQuestionAnswering""",
         """AutoModelForTokenClassification""",
         """AutoModelForUniversalSegmentation""",
         """AutoModelForVideoClassification""",
         """AutoModelForVision2Seq""",
         """AutoModelForVisualQuestionAnswering""",
         """AutoModelForDocumentQuestionAnswering""",
         """AutoModelWithLMHead""",
         """AutoModelForZeroShotImageClassification""",
         """AutoModelForZeroShotObjectDetection""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:				Tuple       							=  [
         """TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
         """TF_MODEL_FOR_CAUSAL_LM_MAPPING""",
         """TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
         """TF_MODEL_FOR_MASK_GENERATION_MAPPING""",
         """TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING""",
         """TF_MODEL_FOR_MASKED_LM_MAPPING""",
         """TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
         """TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
         """TF_MODEL_FOR_PRETRAINING_MAPPING""",
         """TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
         """TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING""",
         """TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING""",
         """TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
         """TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
         """TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
         """TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING""",
         """TF_MODEL_FOR_TEXT_ENCODING_MAPPING""",
         """TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
         """TF_MODEL_FOR_VISION_2_SEQ_MAPPING""",
         """TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING""",
         """TF_MODEL_MAPPING""",
         """TF_MODEL_WITH_LM_HEAD_MAPPING""",
         """TFAutoModel""",
         """TFAutoModelForAudioClassification""",
         """TFAutoModelForCausalLM""",
         """TFAutoModelForImageClassification""",
         """TFAutoModelForMaskedImageModeling""",
         """TFAutoModelForMaskedLM""",
         """TFAutoModelForMaskGeneration""",
         """TFAutoModelForMultipleChoice""",
         """TFAutoModelForNextSentencePrediction""",
         """TFAutoModelForPreTraining""",
         """TFAutoModelForDocumentQuestionAnswering""",
         """TFAutoModelForQuestionAnswering""",
         """TFAutoModelForSemanticSegmentation""",
         """TFAutoModelForSeq2SeqLM""",
         """TFAutoModelForSequenceClassification""",
         """TFAutoModelForSpeechSeq2Seq""",
         """TFAutoModelForTableQuestionAnswering""",
         """TFAutoModelForTextEncoding""",
         """TFAutoModelForTokenClassification""",
         """TFAutoModelForVision2Seq""",
         """TFAutoModelForZeroShotImageClassification""",
         """TFAutoModelWithLMHead""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase:				Any       							=  [
         """FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING""",
         """FLAX_MODEL_FOR_CAUSAL_LM_MAPPING""",
         """FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING""",
         """FLAX_MODEL_FOR_MASKED_LM_MAPPING""",
         """FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING""",
         """FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING""",
         """FLAX_MODEL_FOR_PRETRAINING_MAPPING""",
         """FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING""",
         """FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING""",
         """FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING""",
         """FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING""",
         """FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING""",
         """FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING""",
         """FLAX_MODEL_MAPPING""",
         """FlaxAutoModel""",
         """FlaxAutoModelForCausalLM""",
         """FlaxAutoModelForImageClassification""",
         """FlaxAutoModelForMaskedLM""",
         """FlaxAutoModelForMultipleChoice""",
         """FlaxAutoModelForNextSentencePrediction""",
         """FlaxAutoModelForPreTraining""",
         """FlaxAutoModelForQuestionAnswering""",
         """FlaxAutoModelForSeq2SeqLM""",
         """FlaxAutoModelForSequenceClassification""",
         """FlaxAutoModelForSpeechSeq2Seq""",
         """FlaxAutoModelForTokenClassification""",
         """FlaxAutoModelForVision2Seq""",
     ]


if TYPE_CHECKING:
     from .auto_factory import get_values
     from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
     from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
     from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
     from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
     from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_auto import (
              MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
              MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
              MODEL_FOR_AUDIO_XVECTOR_MAPPING,
              MODEL_FOR_BACKBONE_MAPPING,
              MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
              MODEL_FOR_CAUSAL_LM_MAPPING,
              MODEL_FOR_CTC_MAPPING,
              MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
              MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
              MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
              MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
              MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
              MODEL_FOR_MASK_GENERATION_MAPPING,
              MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
              MODEL_FOR_MASKED_LM_MAPPING,
              MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
              MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
              MODEL_FOR_OBJECT_DETECTION_MAPPING,
              MODEL_FOR_PRETRAINING_MAPPING,
              MODEL_FOR_QUESTION_ANSWERING_MAPPING,
              MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
              MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
              MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
              MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
              MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
              MODEL_FOR_TEXT_ENCODING_MAPPING,
              MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
              MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
              MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
              MODEL_FOR_VISION_2_SEQ_MAPPING,
              MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
              MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
              MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
              MODEL_MAPPING,
              MODEL_WITH_LM_HEAD_MAPPING,
              AutoBackbone,
              AutoModel,
              AutoModelForAudioClassification,
              AutoModelForAudioFrameClassification,
              AutoModelForAudioXVector,
              AutoModelForCausalLM,
              AutoModelForCTC,
              AutoModelForDepthEstimation,
              AutoModelForDocumentQuestionAnswering,
              AutoModelForImageClassification,
              AutoModelForImageSegmentation,
              AutoModelForInstanceSegmentation,
              AutoModelForMaskedImageModeling,
              AutoModelForMaskedLM,
              AutoModelForMaskGeneration,
              AutoModelForMultipleChoice,
              AutoModelForNextSentencePrediction,
              AutoModelForObjectDetection,
              AutoModelForPreTraining,
              AutoModelForQuestionAnswering,
              AutoModelForSemanticSegmentation,
              AutoModelForSeqaSeqLM,
              AutoModelForSequenceClassification,
              AutoModelForSpeechSeqaSeq,
              AutoModelForTableQuestionAnswering,
              AutoModelForTextEncoding,
              AutoModelForTokenClassification,
              AutoModelForUniversalSegmentation,
              AutoModelForVideoClassification,
              AutoModelForVisionaSeq,
              AutoModelForVisualQuestionAnswering,
              AutoModelForZeroShotImageClassification,
              AutoModelForZeroShotObjectDetection,
              AutoModelWithLMHead,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_auto import (
              TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_CAUSAL_LM_MAPPING,
              TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
              TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_MASK_GENERATION_MAPPING,
              TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
              TF_MODEL_FOR_MASKED_LM_MAPPING,
              TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
              TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
              TF_MODEL_FOR_PRETRAINING_MAPPING,
              TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
              TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
              TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
              TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
              TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
              TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
              TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
              TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
              TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
              TF_MODEL_MAPPING,
              TF_MODEL_WITH_LM_HEAD_MAPPING,
              TFAutoModel,
              TFAutoModelForAudioClassification,
              TFAutoModelForCausalLM,
              TFAutoModelForDocumentQuestionAnswering,
              TFAutoModelForImageClassification,
              TFAutoModelForMaskedImageModeling,
              TFAutoModelForMaskedLM,
              TFAutoModelForMaskGeneration,
              TFAutoModelForMultipleChoice,
              TFAutoModelForNextSentencePrediction,
              TFAutoModelForPreTraining,
              TFAutoModelForQuestionAnswering,
              TFAutoModelForSemanticSegmentation,
              TFAutoModelForSeqaSeqLM,
              TFAutoModelForSequenceClassification,
              TFAutoModelForSpeechSeqaSeq,
              TFAutoModelForTableQuestionAnswering,
              TFAutoModelForTextEncoding,
              TFAutoModelForTokenClassification,
              TFAutoModelForVisionaSeq,
              TFAutoModelForZeroShotImageClassification,
              TFAutoModelWithLMHead,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_auto import (
              FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
              FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
              FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
              FLAX_MODEL_FOR_MASKED_LM_MAPPING,
              FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
              FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
              FLAX_MODEL_FOR_PRETRAINING_MAPPING,
              FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
              FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
              FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
              FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
              FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
              FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
              FLAX_MODEL_MAPPING,
              FlaxAutoModel,
              FlaxAutoModelForCausalLM,
              FlaxAutoModelForImageClassification,
              FlaxAutoModelForMaskedLM,
              FlaxAutoModelForMultipleChoice,
              FlaxAutoModelForNextSentencePrediction,
              FlaxAutoModelForPreTraining,
              FlaxAutoModelForQuestionAnswering,
              FlaxAutoModelForSeqaSeqLM,
              FlaxAutoModelForSequenceClassification,
              FlaxAutoModelForSpeechSeqaSeq,
              FlaxAutoModelForTokenClassification,
              FlaxAutoModelForVisionaSeq,
          )

else:
     import sys

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





"""simple docstring"""




import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_UpperCamelCase:				str    							=  logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				42
   _lowerCamelCase												=				None




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase=0.9_9_9	,      _UpperCAmelCase="cosine"	,      )     ->			Optional[Any]:






      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_UpperCAmelCase				):
                  return math.exp(t * -1_2.0				)

      else:
            raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''				)

      lowercase  :      Any					=   []
      for i in range(_UpperCAmelCase				):
            lowercase  :      List[str]					=   i / num_diffusion_timesteps
            lowercase  :      str					=   (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase				) / alpha_bar_fn(_UpperCAmelCase				)	,      _UpperCAmelCase				)				)
      return torch.tensor(_UpperCAmelCase	,      dtype=torch.floataa				)





class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				[e.name for e in KarrasDiffusionSchedulers]
   _lowerCamelCase												=				1


   @register_to_config
   def __init__(							self      :							str,     lowerCAmelCase      :							int = 1000,     lowerCAmelCase      :							float = 0.0001,     lowerCAmelCase      :							float = 0.02,     lowerCAmelCase      :							str = "linear",     lowerCAmelCase      :							Optional[Union[np.ndarray, List[float]]] = None,     lowerCAmelCase      :							str = "epsilon",     lowerCAmelCase      :							str = "linspace",     lowerCAmelCase      :							int = 0,     )    ->       List[Any]:
         if trained_betas is not None:
               lowercase  :      Union[str, Any]					=   torch.tensor(lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "linear":
               lowercase  :      Union[str, Any]					=   torch.linspace(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     dtype=torch.floataa			)
         elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               lowercase  :      str					=   (
                   torch.linspace(beta_start**0.5,     beta_end**0.5,     lowerCAmelCase,     dtype=torch.floataa			) ** 2
               )
         elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               lowercase  :      List[Any]					=   betas_for_alpha_bar(lowerCAmelCase			)
         else:
               raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

         lowercase  :      Any					=   1.0 - self.betas
         lowercase  :      Optional[int]					=   torch.cumprod(self.alphas,     dim=0			)

         lowercase  :      Optional[Any]					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      Optional[int]					=   np.concatenate([sigmas[::-1], [0.0]]			).astype(np.floataa			)
         lowercase  :      int					=   torch.from_numpy(lowerCAmelCase			)

         # setable values
         lowercase  :      List[str]					=   None
         lowercase  :      Tuple					=   np.linspace(0,     num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[::-1].copy()
         lowercase  :      Dict					=   torch.from_numpy(lowerCAmelCase			)
         lowercase  :      str					=   False


   @property
   def        lowercase				(							self      :							Tuple			)    ->       Tuple:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5


   def        lowercase				(							self      :							Any,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor]			)    ->       torch.FloatTensor:
         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      Any					=   timestep.to(self.timesteps.device			)
         lowercase  :      List[Any]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Union[str, Any]					=   self.sigmas[step_index]
         lowercase  :      Dict					=   sample / ((sigma**2 + 1) ** 0.5)
         lowercase  :      Union[str, Any]					=   True
         return sample


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

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
               lowercase  :      int					=   np.linspace(0,     self.config.num_train_timesteps - 1,     lowerCAmelCase,     dtype=lowerCAmelCase			)[
                   ::-1
               ].copy()
         elif self.config.timestep_spacing == "leading":
               lowercase  :      List[str]					=   self.config.num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowercase  :      Any					=   (np.arange(0,     lowerCAmelCase			) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase			)
               timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
               lowercase  :      Optional[Any]					=   self.config.num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowercase  :      Optional[int]					=   (np.arange(self.config.num_train_timesteps,     0,     -step_ratio			)).round().copy().astype(lowerCAmelCase			)
               timesteps -= 1
         else:
               raise ValueError(
                   f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''			)

         lowercase  :      int					=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
         lowercase  :      List[str]					=   np.interp(lowerCAmelCase,     np.arange(0,     len(lowerCAmelCase			)			),     lowerCAmelCase			)
         lowercase  :      Any					=   np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
         lowercase  :      Optional[Any]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)
         if str(lowerCAmelCase			).startswith('mps'			):
               # mps does not support float64
               lowercase  :      List[Any]					=   torch.from_numpy(lowerCAmelCase			).to(lowerCAmelCase,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   torch.from_numpy(lowerCAmelCase			).to(device=lowerCAmelCase			)


   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Union[float, torch.FloatTensor],     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							Optional[torch.Generator] = None,     lowerCAmelCase      :							bool = True,     )    ->       Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

         if (
             isinstance(lowerCAmelCase,     lowerCAmelCase			)
             or isinstance(lowerCAmelCase,     torch.IntTensor			)
             or isinstance(lowerCAmelCase,     torch.LongTensor			)
         ):
               raise ValueError(
                   (
                       'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                       ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
                       ' one of the `scheduler.timesteps` as a timestep.'
                   ),     )

         if not self.is_scale_input_called:
               logger.warning(
                   'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                   'See `StableDiffusionPipeline` for a usage example.'			)

         if isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      List[str]					=   timestep.to(self.timesteps.device			)

         lowercase  :      List[str]					=   (self.timesteps == timestep).nonzero().item()
         lowercase  :      Optional[int]					=   self.sigmas[step_index]

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
               lowercase  :      Optional[Any]					=   sample - sigma * model_output
         elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               lowercase  :      int					=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         elif self.config.prediction_type == "sample":
               raise NotImplementedError('prediction_type not implemented yet: sample'			)
         else:
               raise ValueError(
                   f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

         lowercase  :      List[Any]					=   self.sigmas[step_index]
         lowercase  :      str					=   self.sigmas[step_index + 1]
         lowercase  :      Optional[int]					=   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
         lowercase  :      Optional[int]					=   (sigma_to**2 - sigma_up**2) ** 0.5

         # 2. Convert to an ODE derivative
         lowercase  :      Tuple					=   (sample - pred_original_sample) / sigma

         lowercase  :      str					=   sigma_down - sigma

         lowercase  :      int					=   sample + derivative * dt

         lowercase  :      Dict					=   model_output.device
         lowercase  :      List[Any]					=   randn_tensor(model_output.shape,     dtype=model_output.dtype,     device=lowerCAmelCase,     generator=lowerCAmelCase			)

         lowercase  :      List[str]					=   prev_sample + noise * sigma_up

         if not return_dict:
               return (prev_sample,)

         return EulerAncestralDiscreteSchedulerOutput(
             prev_sample=lowerCAmelCase,     pred_original_sample=lowerCAmelCase			)


   def        lowercase				(							self      :							int,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     lowerCAmelCase      :							torch.FloatTensor,     )    ->       torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         lowercase  :      Tuple					=   self.sigmas.to(device=original_samples.device,     dtype=original_samples.dtype			)
         if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase			):
               # mps does not support float64
               lowercase  :      Dict					=   self.timesteps.to(original_samples.device,     dtype=torch.floataa			)
               lowercase  :      Union[str, Any]					=   timesteps.to(original_samples.device,     dtype=torch.floataa			)
         else:
               lowercase  :      List[str]					=   self.timesteps.to(original_samples.device			)
               lowercase  :      Tuple					=   timesteps.to(original_samples.device			)

         lowercase  :      Union[str, Any]					=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

         lowercase  :      Union[str, Any]					=   sigmas[step_indices].flatten()
         while len(sigma.shape			) < len(original_samples.shape			):
               lowercase  :      Dict					=   sigma.unsqueeze(-1			)

         lowercase  :      Optional[Any]					=   original_samples + noise * sigma
         return noisy_samples






   def __len__(							self      :							int			)    ->       List[str]:
         return self.config.num_train_timesteps
