
from typing import TYPE_CHECKING

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


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






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


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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		42
	_A  =		None


def    UpperCamelCase_ (	a_  ,				a_=0.999  ,				a_="cosine"  ,				)     ->List[Any]:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(a_		):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(a_		):
						return math.exp(t * -12.0		)

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

		A				=[]
		for i in range(a_		):
				A				=i / num_diffusion_timesteps
				A				=(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(a_		) / alpha_bar_fn(a_		)  ,				a_		)		)
		return torch.tensor(a_  ,				dtype=torch.floataa		)






class        UpperCamelCase__(    lowerCAmelCase__						,						lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		[e.name for e in KarrasDiffusionSchedulers]
	_A  =		1







	@register_to_config
	def __init__(      self				:			Any							,			snake_case__				:			int = 10_00							,			snake_case__				:			float = 0.0_001							,			snake_case__				:			float = 0.02							,			snake_case__				:			str = "linear"							,			snake_case__				:			Optional[Union[np.ndarray, List[float]]] = None							,			snake_case__				:			str = "epsilon"							,			snake_case__				:			str = "linear"							,			snake_case__				:			Optional[bool] = False							,			snake_case__				:			str = "linspace"							,			snake_case__				:			int = 0							,			):
			"""simple docstring"""





			if trained_betas is not None:
					A				=torch.tensor(snake_case__							,			dtype=torch.floataa )
			elif beta_schedule == "linear":
					A				=torch.linspace(snake_case__							,			snake_case__							,			snake_case__							,			dtype=torch.floataa )
			elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					A				=(
					    torch.linspace(beta_start**0.5							,			beta_end**0.5							,			snake_case__							,			dtype=torch.floataa ) ** 2
					)
			elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					A				=betas_for_alpha_bar(snake_case__ )
			else:
					raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

			A				=1.0 - self.betas
			A				=torch.cumprod(self.alphas							,			dim=0 )

			A				=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
			A				=np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
			A				=torch.from_numpy(snake_case__ )

			# setable values
			A				=None
			A				=np.linspace(0							,			num_train_timesteps - 1							,			snake_case__							,			dtype=snake_case__ )[::-1].copy()
			A				=torch.from_numpy(snake_case__ )
			A				=False
			A				=use_karras_sigmas







	@property
	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

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







	def        _a					(      self				:			str							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Union[float, torch.FloatTensor] ):
			"""simple docstring"""





			if isinstance(snake_case__							,			torch.Tensor ):
					A				=timestep.to(self.timesteps.device )
			A				=(self.timesteps == timestep).nonzero().item()
			A				=self.sigmas[step_index]

			A				=sample / ((sigma**2 + 1) ** 0.5)

			A				=True
			return sample







	def        _a					(      self				:			str							,			snake_case__				:			int							,			snake_case__				:			Union[str, torch.device] = None ):
			"""simple docstring"""





			A				=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":
					A				=np.linspace(0							,			self.config.num_train_timesteps - 1							,			snake_case__							,			dtype=snake_case__ )[
					    ::-1
					].copy()
			elif self.config.timestep_spacing == "leading":
					A				=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
					A				=(np.arange(0							,			snake_case__ ) * step_ratio).round()[::-1].copy().astype(snake_case__ )
					timesteps += self.config.steps_offset
			elif self.config.timestep_spacing == "trailing":
					A				=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
					A				=(np.arange(self.config.num_train_timesteps							,			0							,			-step_ratio )).round().copy().astype(snake_case__ )
					timesteps -= 1
			else:
					raise ValueError(
					    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

			A				=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
			A				=np.log(snake_case__ )

			if self.config.interpolation_type == "linear":
					A				=np.interp(snake_case__							,			np.arange(0							,			len(snake_case__ ) )							,			snake_case__ )
			elif self.config.interpolation_type == "log_linear":
					A				=torch.linspace(np.log(sigmas[-1] )							,			np.log(sigmas[0] )							,			num_inference_steps + 1 ).exp()
			else:
					raise ValueError(
					    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
					    " 'linear' or 'log_linear'" )

			if self.use_karras_sigmas:
					A				=self._convert_to_karras(in_sigmas=snake_case__							,			num_inference_steps=self.num_inference_steps )
					A				=np.array([self._sigma_to_t(snake_case__							,			snake_case__ ) for sigma in sigmas] )

			A				=np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
			A				=torch.from_numpy(snake_case__ ).to(device=snake_case__ )
			if str(snake_case__ ).startswith("mps" ):
					# mps does not support float64
					A				=torch.from_numpy(snake_case__ ).to(snake_case__							,			dtype=torch.floataa )
			else:
					A				=torch.from_numpy(snake_case__ ).to(device=snake_case__ )







	def        _a					(      self				:			Dict							,			snake_case__				:			Dict							,			snake_case__				:			Any ):
			"""simple docstring"""





			A				=np.log(snake_case__ )

			# get distribution
			A				=log_sigma - log_sigmas[:, np.newaxis]

			# get sigmas range
			A				=np.cumsum((dists >= 0)							,			axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
			A				=low_idx + 1

			A				=log_sigmas[low_idx]
			A				=log_sigmas[high_idx]

			# interpolate sigmas
			A				=(low - log_sigma) / (low - high)
			A				=np.clip(snake_case__							,			0							,			1 )

			# transform interpolation to time range
			A				=(1 - w) * low_idx + w * high_idx
			A				=t.reshape(sigma.shape )
			return t







	def        _a					(      self				:			List[str]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Dict ):
			"""simple docstring"""





			A				=in_sigmas[-1].item()
			A				=in_sigmas[0].item()

			A				=7.0  # 7.0 is the value used in the paper
			A				=np.linspace(0							,			1							,			snake_case__ )
			A				=sigma_min ** (1 / rho)
			A				=sigma_max ** (1 / rho)
			A				=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
			return sigmas







	def        _a					(      self				:			Any							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			Union[float, torch.FloatTensor]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			float = 0.0							,			snake_case__				:			float = 0.0							,			snake_case__				:			float = float("inf" )							,			snake_case__				:			float = 1.0							,			snake_case__				:			Optional[torch.Generator] = None							,			snake_case__				:			bool = True							,			):
			"""simple docstring"""





			if (
			    isinstance(snake_case__							,			snake_case__ )
			    or isinstance(snake_case__							,			torch.IntTensor )
			    or isinstance(snake_case__							,			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(snake_case__							,			torch.Tensor ):
					A				=timestep.to(self.timesteps.device )

			A				=(self.timesteps == timestep).nonzero().item()
			A				=self.sigmas[step_index]

			A				=min(s_churn / (len(self.sigmas ) - 1)							,			2**0.5 - 1 ) if s_tmin <= sigma <= s_tmax else 0.0

			A				=randn_tensor(
			    model_output.shape							,			dtype=model_output.dtype							,			device=model_output.device							,			generator=snake_case__ )

			A				=noise * s_noise
			A				=sigma * (gamma + 1)

			if gamma > 0:
					A				=sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

			# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
			# NOTE: "original_sample" should not be an expected prediction_type but is left in for
			# backwards compatibility
			if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
					A				=model_output
			elif self.config.prediction_type == "epsilon":
					A				=sample - sigma_hat * model_output
			elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					A				=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				=(sample - pred_original_sample) / sigma_hat

			A				=self.sigmas[step_index + 1] - sigma_hat

			A				=sample + derivative * dt

			if not return_dict:
					return (prev_sample,)

			return EulerDiscreteSchedulerOutput(prev_sample=snake_case__							,			pred_original_sample=snake_case__ )







	def        _a					(      self				:			Optional[int]							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			torch.FloatTensor							,			snake_case__				:			torch.FloatTensor							,			):
			"""simple docstring"""





			A				=self.sigmas.to(device=original_samples.device							,			dtype=original_samples.dtype )
			if original_samples.device.type == "mps" and torch.is_floating_point(snake_case__ ):
					# mps does not support float64
					A				=self.timesteps.to(original_samples.device							,			dtype=torch.floataa )
					A				=timesteps.to(original_samples.device							,			dtype=torch.floataa )
			else:
					A				=self.timesteps.to(original_samples.device )
					A				=timesteps.to(original_samples.device )

			A				=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

			A				=sigmas[step_indices].flatten()
			while len(sigma.shape ) < len(original_samples.shape ):
					A				=sigma.unsqueeze(-1 )

			A				=original_samples + noise * sigma
			return noisy_samples







	def __len__(      self				:			Tuple ):
			"""simple docstring"""





			return self.config.num_train_timesteps




