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__							(       __snake_case				,						__snake_case		):
			A__    :       Tuple        =      1



			@register_to_config
			def __init__(						self				,						UpperCAmelCase = 1_0_0_0				,						UpperCAmelCase = None   )			->   List[str]:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(UpperCAmelCase   )

								# standard deviation of the initial noise distribution
								__a		 =       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.
								__a		 =       4

								# running values
								__a		 =       []



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   List[Any]:
								__a		 =       num_inference_steps
								__a		 =       torch.linspace(1				,						0				,						num_inference_steps + 1   )[:-1]
								__a		 =       torch.cat([steps, torch.tensor([0.0]   )]   )

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

								__a		 =       (1.0 - self.betas**2) ** 0.5

								__a		 =       (torch.atana(self.betas				,						self.alphas   ) / math.pi * 2)[:-1]
								__a		 =       timesteps.to(UpperCAmelCase   )

								__a		 =       []



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[SchedulerOutput, Tuple]:
								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'   )

								__a		 =       (self.timesteps == timestep).nonzero().item()
								__a		 =       timestep_index + 1

								__a		 =       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(UpperCAmelCase   )

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

								__a		 =       self._get_prev_sample(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )

								if not return_dict:
													return (prev_sample,)

								return SchedulerOutput(prev_sample=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   torch.FloatTensor:
								return sample



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   str:
								__a		 =       self.alphas[timestep_index]
								__a		 =       self.betas[timestep_index]

								__a		 =       self.alphas[prev_timestep_index]
								__a		 =       self.betas[prev_timestep_index]

								__a		 =       (sample - sigma * ets) / max(UpperCAmelCase				,						1e-8   )
								__a		 =       next_alpha * pred + ets * next_sigma

								return prev_sample







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







from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Dict							=	{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		int							=	[
	    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SwinForImageClassification""",
	    """SwinForMaskedImageModeling""",
	    """SwinModel""",
	    """SwinPreTrainedModel""",
	    """SwinBackbone""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFSwinForImageClassification""",
	    """TFSwinForMaskedImageModeling""",
	    """TFSwinModel""",
	    """TFSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		List[Any]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




