


"""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


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			{
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Any        =   "mobilenet_v2"






       def __init__(   self  :    int  ,     snake_case__  :    Any=3  ,     snake_case__  :    Optional[int]=2_2_4  ,     snake_case__  :    str=1.0  ,     snake_case__  :    Optional[int]=8  ,     snake_case__  :    str=8  ,     snake_case__  :    Tuple=6  ,     snake_case__  :    Any=3_2  ,     snake_case__  :    Dict=True  ,     snake_case__  :    Optional[int]=True  ,     snake_case__  :    Any="relu6"  ,     snake_case__  :    int=True  ,     snake_case__  :    Any=0.8  ,     snake_case__  :    Dict=0.02  ,     snake_case__  :    int=0.0_01  ,     snake_case__  :    List[str]=2_5_5  ,     **snake_case__  :    List[str]  ,     ):

              '''simple docstring'''






              super().__init__(**snake_case__				)

              if depth_multiplier <= 0:
                     raise ValueError('''depth_multiplier must be greater than zero.'''				)

              lowercase		:str       							=				num_channels
              lowercase		:Tuple       							=				image_size
              lowercase		:Dict       							=				depth_multiplier
              lowercase		:List[str]       							=				depth_divisible_by
              lowercase		:Tuple       							=				min_depth
              lowercase		:Tuple       							=				expand_ratio
              lowercase		:Union[str, Any]       							=				output_stride
              lowercase		:List[Any]       							=				first_layer_is_expansion
              lowercase		:Any       							=				finegrained_output
              lowercase		:int       							=				hidden_act
              lowercase		:Optional[Any]       							=				tf_padding
              lowercase		:Optional[Any]       							=				classifier_dropout_prob
              lowercase		:str       							=				initializer_range
              lowercase		:int       							=				layer_norm_eps
              lowercase		:int       							=				semantic_loss_ignore_index







class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Optional[int]        =   version.parse("1.11"     )






       @property
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              return OrderedDict([('''pixel_values''', {0: '''batch'''})]				)






       @property
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              if self.task == "image-classification":
                     return OrderedDict([('''logits''', {0: '''batch'''})]				)
              else:
                     return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]				)







       @property
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return 1e-4





"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      UNetaDModel
       __A			:      ScoreSdeVeScheduler






       def __init__(   self  :    Optional[Any]  ,     snake_case__  :    UNetaDModel  ,     snake_case__  :    ScoreSdeVeScheduler				):

              '''simple docstring'''






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







       @torch.no_grad()
       def __call__(   self  :    Tuple  ,     snake_case__  :    int = 1  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     snake_case__  :    Optional[str] = "pil"  ,     snake_case__  :    bool = True  ,     **snake_case__  :    Optional[Any]  ,     ):

              '''simple docstring'''






              lowercase		:str       							=				self.unet.config.sample_size
              lowercase		:str       							=				(batch_size, 3, img_size, img_size)

              lowercase		:Any       							=				self.unet

              lowercase		:Dict       							=				randn_tensor(snake_case__  ,     generator=snake_case__				) * self.scheduler.init_noise_sigma
              lowercase		:int       							=				sample.to(self.device				)

              self.scheduler.set_timesteps(snake_case__				)
              self.scheduler.set_sigmas(snake_case__				)

              for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
                     lowercase		:Optional[int]       							=				self.scheduler.sigmas[i] * torch.ones(shape[0]  ,     device=self.device				)

                     # correction step
                     for _ in range(self.scheduler.config.correct_steps				):
                            lowercase		:str       							=				self.unet(snake_case__  ,     snake_case__				).sample
                            lowercase		:Optional[Any]       							=				self.scheduler.step_correct(snake_case__  ,     snake_case__  ,     generator=snake_case__				).prev_sample

                     # prediction step
                     lowercase		:Optional[Any]       							=				model(snake_case__  ,     snake_case__				).sample
                     lowercase		:Dict       							=				self.scheduler.step_pred(snake_case__  ,     snake_case__  ,     snake_case__  ,     generator=snake_case__				)

                     lowercase							,  lowercase		:Tuple       							=				output.prev_sample, output.prev_sample_mean

              lowercase		:str       							=				sample_mean.clamp(0  ,     1				)
              lowercase		:int       							=				sample.cpu().permute(0  ,     2  ,     3  ,     1				).numpy()
              if output_type == "pil":
                     lowercase		:str       							=				self.numpy_to_pil(snake_case__				)

              if not return_dict:
                     return (sample,)

              return ImagePipelineOutput(images=snake_case__				)

