



'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__     :  Optional[Any]    					=			logging.get_logger(__name__)

lowercase__     :  Any    					=			{
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}

class       lowerCamelCase_							(	lowerCAmelCase_					,    lowerCAmelCase_  ):






   '''simple docstring'''

   A :     Any				=	"convnext"


   def __init__(  self     ,						__UpperCamelCase=3     ,						__UpperCamelCase=4     ,						__UpperCamelCase=4     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						__UpperCamelCase="gelu"     ,						__UpperCamelCase=0.02     ,						__UpperCamelCase=1e-12     ,						__UpperCamelCase=1e-6     ,						__UpperCamelCase=0.0     ,						__UpperCamelCase=2_24     ,						__UpperCamelCase=None     ,						__UpperCamelCase=None     ,						**__UpperCamelCase     ,						):
       super().__init__(**__UpperCamelCase )

       UpperCamelCase__      =			num_channels
       UpperCamelCase__      =			patch_size
       UpperCamelCase__      =			num_stages
       UpperCamelCase__      =			[96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
       UpperCamelCase__      =			[3, 3, 9, 3] if depths is None else depths
       UpperCamelCase__      =			hidden_act
       UpperCamelCase__      =			initializer_range
       UpperCamelCase__      =			layer_norm_eps
       UpperCamelCase__      =			layer_scale_init_value
       UpperCamelCase__      =			drop_path_rate
       UpperCamelCase__      =			image_size
       UpperCamelCase__      =			["stem"] + [f'stage{idx}' for idx in range(1     ,						len(self.depths ) + 1 )]
       UpperCamelCase__    ,  UpperCamelCase__      =			get_aligned_output_features_output_indices(
           out_features=__UpperCamelCase     ,						out_indices=__UpperCamelCase     ,						stage_names=self.stage_names )

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

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


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







   @property
   def 	_UpperCamelCase       (  self ):
       return 1e-5









'''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       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()

       UpperCamelCase__      =			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     ,						):
       UpperCamelCase__      =			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 ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

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

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




