






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


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   "swiftformer"


 def __init__(		self							,					_A=3							,					_A=[3, 3, 6, 4]							,					_A=[48, 56, 112, 220]							,					_A=4							,					_A=[True, True, True, True]							,					_A="gelu"							,					_A=3							,					_A=2							,					_A=1							,					_A=0.0							,					_A=True							,					_A=1e-5							,					_A=1e-5							,					**_A							,					)	->						List[Any]:

     """simple docstring"""



     super().__init__(**_A)
     _UpperCAmelCase			: Tuple     					=			num_channels
     _UpperCAmelCase			: Optional[int]     					=			depths
     _UpperCAmelCase			: Union[str, Any]     					=			embed_dims
     _UpperCAmelCase			: Dict     					=			mlp_ratio
     _UpperCAmelCase			: Tuple     					=			downsamples
     _UpperCAmelCase			: str     					=			hidden_act
     _UpperCAmelCase			: int     					=			down_patch_size
     _UpperCAmelCase			: Dict     					=			down_stride
     _UpperCAmelCase			: Dict     					=			down_pad
     _UpperCAmelCase			: Tuple     					=			drop_path_rate
     _UpperCAmelCase			: Tuple     					=			use_layer_scale
     _UpperCAmelCase			: int     					=			layer_scale_init_value
     _UpperCAmelCase			: int     					=			batch_norm_eps



class 							A_      (  __lowercase     ):






 '''simple docstring'''




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


 @property
 def 	snake_case__			(		self)	->						Mapping[str, Mapping[int, str]]:

     """simple docstring"""



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


 @property
 def 	snake_case__			(		self)	->						float:

     """simple docstring"""



     return 1e-4












from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A)	->						Dict:

     """simple docstring"""



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


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

     """simple docstring"""



     if isinstance(self.unet.config.sample_size							,					_A):
         _UpperCAmelCase			: List[str]     					=			(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
     else:
         _UpperCAmelCase			: Union[str, Any]     					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
         # randn does not work reproducibly on mps
         _UpperCAmelCase			: int     					=			randn_tensor(_A							,					generator=_A)
         _UpperCAmelCase			: Tuple     					=			image.to(self.device)
     else:
         _UpperCAmelCase			: Any     					=			randn_tensor(_A							,					generator=_A							,					device=self.device)

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

     for t in self.progress_bar(self.scheduler.timesteps):
         # 1. predict noise model_output
         _UpperCAmelCase			: Optional[Any]     					=			self.unet(_A							,					_A).sample

         # 2. compute previous image: x_t -> x_t-1
         _UpperCAmelCase			: Dict     					=			self.scheduler.step(_A							,					_A							,					_A							,					generator=_A).prev_sample

     _UpperCAmelCase			: Optional[int]     					=			(image / 2 + 0.5).clamp(0							,					1)
     _UpperCAmelCase			: int     					=			image.cpu().permute(0							,					2							,					3							,					1).numpy()
     if output_type == "pil":
         _UpperCAmelCase			: str     					=			self.numpy_to_pil(_A)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_A)




