






'''simple docstring'''
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							={
    "MBZUAI/swiftformer-xs": "https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json",
}


class    _snake_case   (				SCREAMING_SNAKE_CASE__					):

					"""simple docstring"""





					_UpperCamelCase		 =      "swiftformer"




					def __init__(     self   , UpperCAmelCase__=3   , UpperCAmelCase__=[3, 3, 6, 4]   , UpperCAmelCase__=[48, 56, 112, 220]   , UpperCAmelCase__=4   , UpperCAmelCase__=[True, True, True, True]   , UpperCAmelCase__="gelu"   , UpperCAmelCase__=3   , UpperCAmelCase__=2   , UpperCAmelCase__=1   , UpperCAmelCase__=0.0   , UpperCAmelCase__=True   , UpperCAmelCase__=1e-5   , UpperCAmelCase__=1e-5   , **UpperCAmelCase__   , )      ->	str:
										super().__init__(**UpperCAmelCase__     )
										a_									=   num_channels
										a_									=   depths
										a_									=   embed_dims
										a_									=   mlp_ratio
										a_									=   downsamples
										a_									=   hidden_act
										a_									=   down_patch_size
										a_									=   down_stride
										a_									=   down_pad
										a_									=   drop_path_rate
										a_									=   use_layer_scale
										a_									=   layer_scale_init_value
										a_									=   batch_norm_eps



class    _snake_case   (				SCREAMING_SNAKE_CASE__					):

					"""simple docstring"""





					_UpperCamelCase		 =      version.parse("1.11"					)




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




					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	float:
										return 1e-4









'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42




					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	int:
										super().__init__()

										a_									=   PNDMScheduler.from_config(scheduler.config     )

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




					@torch.no_grad()
					def __call__(     self   , UpperCAmelCase__ = 1   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = None   , UpperCAmelCase__ = "pil"   , UpperCAmelCase__ = True   , **UpperCAmelCase__   , )      ->	Union[ImagePipelineOutput, Tuple]:
										a_									=   randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   , generator=UpperCAmelCase__   , device=self.device   , )

										self.scheduler.set_timesteps(UpperCAmelCase__     )
										for t in self.progress_bar(self.scheduler.timesteps     ):
															a_									=   self.unet(UpperCAmelCase__   , UpperCAmelCase__     ).sample

															a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										a_									=   (image / 2 + 0.5).clamp(0   , 1     )
										a_									=   image.cpu().permute(0   , 2   , 3   , 1     ).numpy()
										if output_type == "pil":
															a_									=   self.numpy_to_pil(UpperCAmelCase__     )

										if not return_dict:
															return (image,)

										return ImagePipelineOutput(images=UpperCAmelCase__     )

