"""simple docstring"""






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


_A   					=    logging.get_logger(__name__)

_A   					=    {
    'facebook/convnextv2-tiny-1k-224': 'https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json',
}

class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		"convnextv2"



					def __init__(						self			:				List[Any]   , _snake_case			:				Tuple=3   , _snake_case			:				Tuple=4   , _snake_case			:				Union[str, Any]=4   , _snake_case			:				List[Any]=None   , _snake_case			:				Union[str, Any]=None   , _snake_case			:				List[Any]="gelu"   , _snake_case			:				Union[str, Any]=0.02   , _snake_case			:				Optional[int]=1e-12   , _snake_case			:				List[Any]=0.0   , _snake_case			:				Union[str, Any]=224   , _snake_case			:				Dict=None   , _snake_case			:				str=None   , **_snake_case			:				List[Any]   , )							->       Tuple:
									super().__init__(**_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				num_channels
									SCREAMING_SNAKE_CASE__		  =				patch_size
									SCREAMING_SNAKE_CASE__		  =				num_stages
									SCREAMING_SNAKE_CASE__		  =				[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
									SCREAMING_SNAKE_CASE__		  =				[3, 3, 9, 3] if depths is None else depths
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				layer_norm_eps
									SCREAMING_SNAKE_CASE__		  =				drop_path_rate
									SCREAMING_SNAKE_CASE__		  =				image_size
									SCREAMING_SNAKE_CASE__		  =				["stem"] + [F"""stage{idx}""" for idx in range(1   , len(self.depths		) + 1		)]
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				get_aligned_output_features_output_indices(
									    out_features=_snake_case   , out_indices=_snake_case   , 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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				Optional[int]   , _snake_case			:				Dict		)							->       Dict:
									super().__init__()

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

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



					@torch.no_grad()
					def __call__(						self			:				int   , _snake_case			:				int = 1   , _snake_case			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   , _snake_case			:				float = 0.0   , _snake_case			:				int = 50   , _snake_case			:				Optional[bool] = None   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , )							->       Union[ImagePipelineOutput, Tuple]:

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													SCREAMING_SNAKE_CASE__		  =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

									SCREAMING_SNAKE_CASE__		  =				randn_tensor(_snake_case   , generator=_snake_case   , device=self.device   , dtype=self.unet.dtype		)

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

									for t in self.progress_bar(self.scheduler.timesteps		):
													# 1. predict noise model_output
													SCREAMING_SNAKE_CASE__		  =				self.unet(_snake_case   , _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
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(
													    _snake_case   , _snake_case   , _snake_case   , eta=_snake_case   , use_clipped_model_output=_snake_case   , generator=_snake_case		).prev_sample

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

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)




