






"""simple docstring"""







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


_lowerCAmelCase            =	logging.get_logger(__name__)

_lowerCAmelCase            =	{
    '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      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : List[Any]												=		"""segformer"""






						def __init__(		self  :int			, __magic_name__  :str=3			, __magic_name__  :Tuple=4			, __magic_name__  :List[Any]=[2, 2, 2, 2]			, __magic_name__  :Tuple=[8, 4, 2, 1]			, __magic_name__  :Optional[Any]=[32, 64, 160, 256]			, __magic_name__  :str=[7, 3, 3, 3]			, __magic_name__  :List[Any]=[4, 2, 2, 2]			, __magic_name__  :Dict=[1, 2, 5, 8]			, __magic_name__  :Tuple=[4, 4, 4, 4]			, __magic_name__  :Tuple="gelu"			, __magic_name__  :int=0.0			, __magic_name__  :int=0.0			, __magic_name__  :Any=0.1			, __magic_name__  :str=0.02			, __magic_name__  :List[str]=0.1			, __magic_name__  :Optional[int]=1E-6			, __magic_name__  :List[Any]=256			, __magic_name__  :Union[str, Any]=255			, **__magic_name__  :Tuple			, )      ->Tuple:
										super().__init__(**__magic_name__				)

										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."""			, __magic_name__			, )

										lowercase    :      str       = num_channels
										lowercase    :      Optional[int]       = num_encoder_blocks
										lowercase    :      Union[str, Any]       = depths
										lowercase    :      Optional[Any]       = sr_ratios
										lowercase    :      Optional[Any]       = hidden_sizes
										lowercase    :      Optional[Any]       = patch_sizes
										lowercase    :      Any       = strides
										lowercase    :      Union[str, Any]       = mlp_ratios
										lowercase    :      List[str]       = num_attention_heads
										lowercase    :      List[Any]       = hidden_act
										lowercase    :      int       = hidden_dropout_prob
										lowercase    :      str       = attention_probs_dropout_prob
										lowercase    :      Any       = classifier_dropout_prob
										lowercase    :      Dict       = initializer_range
										lowercase    :      Optional[int]       = drop_path_rate
										lowercase    :      Union[str, Any]       = layer_norm_eps
										lowercase    :      List[Any]       = decoder_hidden_size
										lowercase    :      Union[str, Any]       = kwargs.get("""reshape_last_stage"""			, __magic_name__				)
										lowercase    :      str       = semantic_loss_ignore_index


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : List[Any]												=		version.parse("""1.11"""     )






						@property
						def      __snake_case      (		self  :Dict				)      ->Mapping[str, Mapping[int, str]]:
										return OrderedDict(
										    [
										        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
										    ]				)






						@property
						def      __snake_case      (		self  :List[str]				)      ->float:
										return 1E-4






						@property
						def      __snake_case      (		self  :str				)      ->int:
										return 12








"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Dict:
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase    :      Optional[Any]       = DDIMScheduler.from_config(scheduler.config				)

										self.register_modules(unet=__magic_name__			, scheduler=__magic_name__				)






						@torch.no_grad()
						def __call__(		self  :Optional[int]			, __magic_name__  :int = 1			, __magic_name__  :Optional[Union[torch.Generator, List[torch.Generator]]] = None			, __magic_name__  :float = 0.0			, __magic_name__  :int = 50			, __magic_name__  :Optional[bool] = None			, __magic_name__  :Optional[str] = "pil"			, __magic_name__  :bool = True			, )      ->Union[ImagePipelineOutput, Tuple]:

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size			, __magic_name__				):
														lowercase    :      int       = (
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
										else:
														lowercase    :      Tuple       = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(__magic_name__			, __magic_name__				) and len(__magic_name__				) != batch_size:
														raise ValueError(
														    f"""You have passed a list of generators of length {len(__magic_name__				)}, but requested an effective batch"""
														    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

										lowercase    :      Dict       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

										# set step values
										self.scheduler.set_timesteps(__magic_name__				)

										for t in self.progress_bar(self.scheduler.timesteps				):
														# 1. predict noise model_output
														lowercase    :      Union[str, Any]       = self.unet(__magic_name__			, __magic_name__				).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														lowercase    :      int       = self.scheduler.step(
														    __magic_name__			, __magic_name__			, __magic_name__			, eta=__magic_name__			, use_clipped_model_output=__magic_name__			, generator=__magic_name__				).prev_sample

										lowercase    :      Optional[Any]       = (image / 2 + 0.5).clamp(0			, 1				)
										lowercase    :      Tuple       = image.cpu().permute(0			, 2			, 3			, 1				).numpy()
										if output_type == "pil":
														lowercase    :      Any       = self.numpy_to_pil(__magic_name__				)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)
