

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase  :		str  =		logging.get_logger(__name__)

_UpperCAmelCase  :		str  =		{
    '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
    # See all Nat models at https://huggingface.co/models?filter=nat
}

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'nat'

 UpperCamelCase__     =       {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }







 def __init__(       self  , snake_case_=4  , snake_case_=3  , snake_case_=64  , snake_case_=[3, 4, 6, 5]  , snake_case_=[2, 4, 8, 16]  , snake_case_=7  , snake_case_=3.0  , snake_case_=True  , snake_case_=0.0  , snake_case_=0.0  , snake_case_=0.1  , snake_case_="gelu"  , snake_case_=0.02  , snake_case_=1E-5  , snake_case_=0.0  , snake_case_=None  , snake_case_=None  , **snake_case_  , ):
   super().__init__(**snake_case_   )

   lowercase									=patch_size
   lowercase									=num_channels
   lowercase									=embed_dim
   lowercase									=depths
   lowercase									=len(snake_case_   )
   lowercase									=num_heads
   lowercase									=kernel_size
   lowercase									=mlp_ratio
   lowercase									=qkv_bias
   lowercase									=hidden_dropout_prob
   lowercase									=attention_probs_dropout_prob
   lowercase									=drop_path_rate
   lowercase									=hidden_act
   lowercase									=layer_norm_eps
   lowercase									=initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   lowercase									=int(embed_dim * 2 ** (len(snake_case_   ) - 1)   )
   lowercase									=layer_scale_init_value
   lowercase									=['''stem'''] + [f'stage{idx}' for idx in range(1  , len(snake_case_   ) + 1   )]
   lowercase							, lowercase									=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      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__   )

