

from typing import List, Optional, Tuple, Union

import torch

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

class    a__  (   __lowerCAmelCase     ):
			"""simple docstring"""







			def __init__(      self , lowercase , lowercase    )					->  List[str]:
							'''simple docstring'''






							super().__init__()
							self.register_modules(unet=__SCREAMING_SNAKE_CASE , scheduler=__SCREAMING_SNAKE_CASE    )


			@torch.no_grad()
			def __call__(      self , lowercase = 1 , lowercase = None , lowercase = 1000 , lowercase = "pil" , lowercase = True , )					->  Union[ImagePipelineOutput, Tuple]:
							'''simple docstring'''






							if isinstance(self.unet.config.sample_size , __SCREAMING_SNAKE_CASE    ):
											A__            =     (
											    batch_size,
											    self.unet.config.in_channels,
											    self.unet.config.sample_size,
											    self.unet.config.sample_size,
											)
							else:
											A__            =     (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
											# randn does not work reproducibly on mps
											A__            =     randn_tensor(__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE    )
											A__            =     image.to(self.device    )
							else:
											A__            =     randn_tensor(__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , device=self.device    )

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

							for t in self.progress_bar(self.scheduler.timesteps    ):
											# 1. predict noise model_output
											A__            =     self.unet(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE    ).sample

											# 2. compute previous image: x_t -> x_t-1
											A__            =     self.scheduler.step(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE    ).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(__SCREAMING_SNAKE_CASE    )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE    )



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    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    'swiftformer'


			def __init__(      self , lowercase=3 , lowercase=[3, 3, 6, 4] , lowercase=[48, 56, 112, 220] , lowercase=4 , lowercase=[True, True, True, True] , lowercase="gelu" , lowercase=3 , lowercase=2 , lowercase=1 , lowercase=0.0 , lowercase=True , lowercase=1e-5 , lowercase=1e-5 , **lowercase , )					->  Optional[int]:
							'''simple docstring'''






							super().__init__(**lowercase    )
							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    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    version.parse('1.11'     )


			@property
			def        UpperCamelCase						(      self    )					->  Mapping[str, Mapping[int, str]]:
							'''simple docstring'''






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


			@property
			def        UpperCamelCase						(      self    )					->  float:
							'''simple docstring'''






							return 1e-4

