
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		(   __UpperCAmelCase      ,	__UpperCAmelCase					):
							lowercase_		:						str   							=  1







							@register_to_config
							def __init__(						self  :  Tuple      ,				snake_case__  :  int = 1_000      ,				snake_case__  :  Optional[Union[np.ndarray, List[float]]] = None ):
								"""simple docstring"""







								self.set_timesteps(snake_case__ )

								# standard deviation of the initial noise distribution
								__lowerCAmelCase         =							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         =							4

								# running values
								__lowerCAmelCase         =							[]







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







								__lowerCAmelCase         =							num_inference_steps
								__lowerCAmelCase         =							torch.linspace(1      ,				0      ,				num_inference_steps + 1 )[:-1]
								__lowerCAmelCase         =							torch.cat([steps, torch.tensor([0.0] )] )

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

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

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

								__lowerCAmelCase         =							[]







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  int      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  bool = True      ,				):
								"""simple docstring"""







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

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

								if len(self.ets ) == 1:
									__lowerCAmelCase         =							self.ets[-1]
								elif len(self.ets ) == 2:
									__lowerCAmelCase         =							(3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets ) == 3:
									__lowerCAmelCase         =							(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
								else:
									__lowerCAmelCase         =							(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

								__lowerCAmelCase         =							self._get_prev_sample(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__ )

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=snake_case__ )







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  torch.FloatTensor      ,				*snake_case__  :  Dict      ,				**snake_case__  :  int ):
								"""simple docstring"""







								return sample







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  List[str]      ,				snake_case__  :  str      ,				snake_case__  :  int      ,				snake_case__  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.alphas[timestep_index]
								__lowerCAmelCase         =							self.betas[timestep_index]

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

								__lowerCAmelCase         =							(sample - sigma * ets) / max(snake_case__      ,				1E-8 )
								__lowerCAmelCase         =							next_alpha * pred + ets * next_sigma

								return prev_sample







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







								return self.config.num_train_timesteps








import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'segformer'







							def __init__(						self  :  List[str]      ,				snake_case__  :  Any=3      ,				snake_case__  :  int=4      ,				snake_case__  :  Tuple=[2, 2, 2, 2]      ,				snake_case__  :  Optional[int]=[8, 4, 2, 1]      ,				snake_case__  :  Union[str, Any]=[32, 64, 160, 256]      ,				snake_case__  :  str=[7, 3, 3, 3]      ,				snake_case__  :  List[Any]=[4, 2, 2, 2]      ,				snake_case__  :  Tuple=[1, 2, 5, 8]      ,				snake_case__  :  List[str]=[4, 4, 4, 4]      ,				snake_case__  :  Optional[Any]="gelu"      ,				snake_case__  :  Optional[Any]=0.0      ,				snake_case__  :  Any=0.0      ,				snake_case__  :  str=0.1      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Any=0.1      ,				snake_case__  :  List[Any]=1E-6      ,				snake_case__  :  Any=256      ,				snake_case__  :  Optional[Any]=255      ,				**snake_case__  :  Union[str, Any]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )

								if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
									warnings.warn(
									    "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
									    " removed, as the behaviour will default to that of reshape_last_stage = True."      ,				snake_case__      ,				)

								__lowerCAmelCase         =							num_channels
								__lowerCAmelCase         =							num_encoder_blocks
								__lowerCAmelCase         =							depths
								__lowerCAmelCase         =							sr_ratios
								__lowerCAmelCase         =							hidden_sizes
								__lowerCAmelCase         =							patch_sizes
								__lowerCAmelCase         =							strides
								__lowerCAmelCase         =							mlp_ratios
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							classifier_dropout_prob
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							drop_path_rate
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							decoder_hidden_size
								__lowerCAmelCase         =							kwargs.get("reshape_last_stage"      ,				snake_case__ )
								__lowerCAmelCase         =							semantic_loss_ignore_index

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  version.parse('1.11'					)







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ] )







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return 1E-4







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return 12






