






'''simple docstring'''







from typing import TYPE_CHECKING

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


_A      :     int       	={
    "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:
			_A      :     Optional[int]       	=[
			    "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:
			_A      :     int       	=[
			    "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:
			_A      :     Optional[int]       	=[
			    "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

			_A      :     Optional[int]       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""






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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)


@flax.struct.dataclass
class 	A_       :





  """simple docstring"""

  lowercase		:	CommonSchedulerState

  # setable values
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	jnp.ndarray
  lowercase		:	Optional[int]   =						None

  # running values
  lowercase		:	Optional[jnp.ndarray]   =						None
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     return cls(common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase						)



@dataclass
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	LMSDiscreteSchedulerState



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	int   =						[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowercase		:	jnp.dtype
  @property
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     return True
  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = 10_00  ,					__UpperCAmelCase = 0.0001  ,					__UpperCAmelCase = 0.02  ,					__UpperCAmelCase = "linear"  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "epsilon"  ,					__UpperCAmelCase = jnp.floataa  ,					)				->					Union[str, Any]:
     a   :	Dict						=    dtype
  def 							lowercase_     (						self  ,					__UpperCAmelCase = None						)				->					LMSDiscreteSchedulerState:
     if common is None:
        a   :	Dict						=    CommonSchedulerState.create(self						)

     a   :	List[Any]						=    jnp.arange(0  ,					self.config.num_train_timesteps						).round()[::-1]
     a   :	Dict						=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

     # standard deviation of the initial noise distribution
     a   :	Union[str, Any]						=    sigmas.max()

     return LMSDiscreteSchedulerState.create(
         common=__UpperCAmelCase  ,					init_noise_sigma=__UpperCAmelCase  ,					timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					jnp.ndarray:
     ((a)	, )   :	Optional[int]						=    jnp.where(state.timesteps == timestep  ,					size=1						)
     a   :	Optional[Any]						=    step_index[0]

     a   :	Union[str, Any]						=    state.sigmas[step_index]
     a   :	Optional[int]						=    sample / ((sigma**2 + 1) ** 0.5)
     return sample
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:

     def lms_derivative(__UpperCAmelCase						):
        a   :	Optional[Any]						=    1.0
        for k in range(__UpperCAmelCase						):
           if current_order == k:
              continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

     a   :	Union[str, Any]						=    integrate.quad(__UpperCAmelCase  ,					state.sigmas[t]  ,					state.sigmas[t + 1]  ,					epsrel=1E-4						)[0]

     return integrated_coeff
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = ()						)				->					LMSDiscreteSchedulerState:
     a   :	Optional[int]						=    jnp.linspace(self.config.num_train_timesteps - 1  ,					0  ,					__UpperCAmelCase  ,					dtype=self.dtype						)

     a   :	Dict						=    jnp.floor(__UpperCAmelCase						).astype(jnp.intaa						)
     a   :	str						=    jnp.ceil(__UpperCAmelCase						).astype(jnp.intaa						)

     a   :	List[Any]						=    jnp.mod(__UpperCAmelCase  ,					1.0						)

     a   :	Optional[int]						=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
     a   :	int						=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
     a   :	Optional[int]						=    jnp.concatenate([sigmas, jnp.array([0.0]  ,					dtype=self.dtype						)]						)

     a   :	Any						=    timesteps.astype(jnp.intaa						)

     # initial running values
     a   :	Optional[int]						=    jnp.zeros((0,) + shape  ,					dtype=self.dtype						)

     return state.replace(
         timesteps=__UpperCAmelCase  ,					sigmas=__UpperCAmelCase  ,					num_inference_steps=__UpperCAmelCase  ,					derivatives=__UpperCAmelCase  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = 4  ,					__UpperCAmelCase = True  ,					)				->					Union[FlaxLMSSchedulerOutput, Tuple]:
     if state.num_inference_steps is None:
        raise ValueError(
            'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'						)

     a   :	Dict						=    state.sigmas[timestep]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        a   :	Any						=    sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a   :	str						=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     else:
        raise ValueError(
            f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'						)

     # 2. Convert to an ODE derivative
     a   :	str						=    (sample - pred_original_sample) / sigma
     a   :	int						=    state.replace(derivatives=jnp.append(state.derivatives  ,					__UpperCAmelCase						)						)
     if len(state.derivatives						) > order:
        a   :	Any						=    state.replace(derivatives=jnp.delete(state.derivatives  ,					0						)						)

     # 3. Compute linear multistep coefficients
     a   :	Any						=    min(timestep + 1  ,					__UpperCAmelCase						)
     a   :	Dict						=    [self.get_lms_coefficient(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						) for curr_order in range(__UpperCAmelCase						)]

     # 4. Compute previous sample based on the derivatives path
     a   :	int						=    sample + sum(
         coeff * derivative for coeff, derivative in zip(__UpperCAmelCase  ,					reversed(state.derivatives						)						)						)

     if not return_dict:
        return (prev_sample, state)

     return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase  ,					state=__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					)				->					jnp.ndarray:
     a   :	Dict						=    state.sigmas[timesteps].flatten()
     a   :	Union[str, Any]						=    broadcast_to_shape_from_left(__UpperCAmelCase  ,					noise.shape						)

     a   :	List[Any]						=    original_samples + noise * sigma

     return noisy_samples






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





