

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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class 	A_		(	SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  List[str]										=				1
							@register_to_config
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      int = 1_0_0_0       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[np.ndarray, List[float]]] = None):
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(SCREAMING_SNAKE_CASE__)

								# standard deviation of the initial noise distribution
								__lowerCamelCase    :    int 	=			1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								__lowerCamelCase    :    int 	=			4

								# running values
								__lowerCamelCase    :    Tuple 	=			[]
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Union[str, torch.device] = None):
								__lowerCamelCase    :    List[str] 	=			num_inference_steps
								__lowerCamelCase    :    int 	=			torch.linspace(1       ,0       ,num_inference_steps + 1)[:-1]
								__lowerCamelCase    :    Dict 	=			torch.cat([steps, torch.tensor([0.0])])

								if self.config.trained_betas is not None:
									__lowerCamelCase    :    Any 	=			torch.tensor(self.config.trained_betas       ,dtype=torch.floataa)
								else:
									__lowerCamelCase    :    Dict 	=			torch.sin(steps * math.pi / 2) ** 2

								__lowerCamelCase    :    Optional[int] 	=			(1.0 - self.betas**2) ** 0.5

								__lowerCamelCase    :    Union[str, Any] 	=			(torch.atana(self.betas       ,self.alphas) / math.pi * 2)[:-1]
								__lowerCamelCase    :    Tuple 	=			timesteps.to(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			[]
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      torch.FloatTensor       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      torch.FloatTensor       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,):
								if self.num_inference_steps is None:
									raise ValueError(
									    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')

								__lowerCamelCase    :    Tuple 	=			(self.timesteps == timestep).nonzero().item()
								__lowerCamelCase    :    int 	=			timestep_index + 1

								__lowerCamelCase    :    str 	=			sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(SCREAMING_SNAKE_CASE__)

								if len(self.ets) == 1:
									__lowerCamelCase    :    List[Any] 	=			self.ets[-1]
								elif len(self.ets) == 2:
									__lowerCamelCase    :    str 	=			(3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets) == 3:
									__lowerCamelCase    :    Dict 	=			(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
								else:
									__lowerCamelCase    :    Optional[Any] 	=			(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

								__lowerCamelCase    :    Tuple 	=			self._get_prev_sample(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      torch.FloatTensor       ,*SCREAMING_SNAKE_CASE__		:      Any       ,**SCREAMING_SNAKE_CASE__		:      List[str]):
								return sample
							def    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    Any 	=			self.alphas[timestep_index]
								__lowerCamelCase    :    Dict 	=			self.betas[timestep_index]

								__lowerCamelCase    :    Optional[int] 	=			self.alphas[prev_timestep_index]
								__lowerCamelCase    :    str 	=			self.betas[prev_timestep_index]

								__lowerCamelCase    :    Any 	=			(sample - sigma * ets) / max(SCREAMING_SNAKE_CASE__       ,1E-8)
								__lowerCamelCase    :    Tuple 	=			next_alpha * pred + ets * next_sigma

								return prev_sample


							def __len__(		self		:      Union[str, Any]):
								return self.config.num_train_timesteps







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		={
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

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




