






'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class UpperCAmelCase_       (     __A					,							__A					):




      """simple docstring"""




      UpperCamelCase_             =      '''bit'''
      UpperCamelCase_             =      ['''preactivation''', '''bottleneck''']
      UpperCamelCase_             =      ['''SAME''', '''VALID''']






      def __init__(			self      :		Any  ,   UpperCAmelCase      :		Optional[int]=3  ,   UpperCAmelCase      :		Any=64  ,   UpperCAmelCase      :		List[str]=[256, 512, 1024, 2048]  ,   UpperCAmelCase      :		Dict=[3, 4, 6, 3]  ,   UpperCAmelCase      :		List[Any]="preactivation"  ,   UpperCAmelCase      :		Tuple="relu"  ,   UpperCAmelCase      :		Tuple=None  ,   UpperCAmelCase      :		Tuple=32  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		Dict=32  ,   UpperCAmelCase      :		Any=1  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   **UpperCAmelCase      :		str  ,   )	->	Dict:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       if layer_type not in self.layer_types:
        raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
       if global_padding is not None:
        if global_padding.upper() in self.supported_padding:
         lowercase       : Any						=global_padding.upper()
        else:
         raise ValueError(f'Padding strategy {global_padding} not supported'		)
       lowercase       : List[Any]						=num_channels
       lowercase       : List[str]						=embedding_size
       lowercase       : Union[str, Any]						=hidden_sizes
       lowercase       : Union[str, Any]						=depths
       lowercase       : Optional[int]						=layer_type
       lowercase       : Optional[Any]						=hidden_act
       lowercase       : List[str]						=global_padding
       lowercase       : Dict						=num_groups
       lowercase       : Optional[Any]						=drop_path_rate
       lowercase       : Union[str, Any]						=embedding_dynamic_padding
       lowercase       : Any						=output_stride
       lowercase       : Union[str, Any]						=width_factor

       lowercase       : Union[str, Any]						=['''stem'''] + [f'stage{idx}' for idx in range(1  ,   len(UpperCAmelCase		) + 1		)]
       lowercase	,		lowercase       : List[str]						=get_aligned_output_features_output_indices(
           out_features=UpperCAmelCase  ,   out_indices=UpperCAmelCase  ,   stage_names=self.stage_names		)







"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__()

					# make sure scheduler can always be converted to DDIM
					_UpperCamelCase					:int					     =	DDIMScheduler.from_config(scheduler.config   )

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





		@torch.no_grad()
		def __call__( self					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = 0.0					,				SCREAMING_SNAKE_CASE__ = 50					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "pil"					,				SCREAMING_SNAKE_CASE__ = True					,				)      ->	Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""



					if isinstance(self.unet.config.sample_size					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					:Optional[int]					     =	(
								    batch_size,
								    self.unet.config.in_channels,
								    self.unet.config.sample_size,
								    self.unet.config.sample_size,
								)
					else:
								_UpperCamelCase					:Tuple					     =	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) and len(SCREAMING_SNAKE_CASE__   ) != batch_size:
								raise ValueError(
								    f"You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__   )}, but requested an effective batch"
								    f" size of {batch_size}. Make sure the batch size matches the length of the generators."   )

					_UpperCamelCase					:Optional[int]					     =	randn_tensor(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__					,				device=self.device					,				dtype=self.unet.dtype   )

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

					for t in self.progress_bar(self.scheduler.timesteps   ):
								# 1. predict noise model_output
								_UpperCamelCase					:List[str]					     =	self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								_UpperCamelCase					:Optional[Any]					     =	self.scheduler.step(
								    SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				eta=SCREAMING_SNAKE_CASE__					,				use_clipped_model_output=SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

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

					if not return_dict:
								return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )

