





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	42
 UpperCAmelCase__									=	42
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   UNetaDModel    ,  UpperCAmelCase						:   KarrasVeScheduler			)    ->			List[str]:
     super().__init__()
     self.register_modules(unet=UpperCAmelCase    ,  scheduler=UpperCAmelCase			)







 @torch.no_grad()
 def __call__(   self						:   Optional[int]    ,  UpperCAmelCase						:   int = 1    ,  UpperCAmelCase						:   int = 50    ,  UpperCAmelCase						:   Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,  UpperCAmelCase						:   Optional[str] = "pil"    ,  UpperCAmelCase						:   bool = True    ,  **UpperCAmelCase						:   Union[str, Any]    ,  )    ->			Union[Tuple, ImagePipelineOutput]:
     lowerCamelCase__					:   Any      				=	self.unet.config.sample_size
     lowerCamelCase__					:   Union[str, Any]      				=	(batch_size, 3, img_size, img_size)

     lowerCamelCase__					:   Tuple      				=	self.unet

     # sample x_0 ~ N(0, sigma_0^2 * I)
     lowerCamelCase__					:   Optional[int]      				=	randn_tensor(UpperCAmelCase    ,  generator=UpperCAmelCase    ,  device=self.device			) * self.scheduler.init_noise_sigma

     self.scheduler.set_timesteps(UpperCAmelCase			)

     for t in self.progress_bar(self.scheduler.timesteps			):
         # here sigma_t == t_i from the paper
         lowerCamelCase__					:   Union[str, Any]      				=	self.scheduler.schedule[t]
         lowerCamelCase__					:   Optional[Any]      				=	self.scheduler.schedule[t - 1] if t > 0 else 0

         # 1. Select temporarily increased noise level sigma_hat
         # 2. Add new noise to move from sample_i to sample_hat
         lowerCamelCase__				,							lowerCamelCase__					:   List[Any]      				=	self.scheduler.add_noise_to_input(UpperCAmelCase    ,  UpperCAmelCase    ,  generator=UpperCAmelCase			)

         # 3. Predict the noise residual given the noise magnitude `sigma_hat`
         # The model inputs and output are adjusted by following eq. (213) in [1].
         lowerCamelCase__					:   List[str]      				=	(sigma_hat / 2) * model((sample_hat + 1) / 2    ,  sigma_hat / 2			).sample

         # 4. Evaluate dx/dt at sigma_hat
         # 5. Take Euler step from sigma to sigma_prev
         lowerCamelCase__					:   Tuple      				=	self.scheduler.step(UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase			)

         if sigma_prev != 0:
             # 6. Apply 2nd order correction
             # The model inputs and output are adjusted by following eq. (213) in [1].
             lowerCamelCase__					:   Union[str, Any]      				=	(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2    ,  sigma_prev / 2			).sample
             lowerCamelCase__					:   Union[str, Any]      				=	self.scheduler.step_correct(
                 UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  UpperCAmelCase    ,  step_output.prev_sample    ,  step_output['derivative']    ,  )
         lowerCamelCase__					:   str      				=	step_output.prev_sample

     lowerCamelCase__					:   Union[str, Any]      				=	(sample / 2 + 0.5).clamp(0    ,  1			)
     lowerCamelCase__					:   Tuple      				=	sample.cpu().permute(0    ,  2    ,  3    ,  1			).numpy()
     if output_type == "pil":
         lowerCamelCase__					:   List[Any]      				=	self.numpy_to_pil(UpperCAmelCase			)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=UpperCAmelCase			)












from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase       : Dict			     =       logging.get_logger(__name__)

_UpperCAmelCase       : Union[str, Any]			     =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	"""donut-swin"""

 UpperCAmelCase__									=	{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }
 def __init__(   self						:   Tuple    ,  UpperCAmelCase						:   List[str]=224    ,  UpperCAmelCase						:   Optional[Any]=4    ,  UpperCAmelCase						:   Union[str, Any]=3    ,  UpperCAmelCase						:   Union[str, Any]=96    ,  UpperCAmelCase						:   int=[2, 2, 6, 2]    ,  UpperCAmelCase						:   Dict=[3, 6, 12, 24]    ,  UpperCAmelCase						:   Any=7    ,  UpperCAmelCase						:   Optional[int]=4.0    ,  UpperCAmelCase						:   List[Any]=True    ,  UpperCAmelCase						:   Any=0.0    ,  UpperCAmelCase						:   Optional[Any]=0.0    ,  UpperCAmelCase						:   int=0.1    ,  UpperCAmelCase						:   List[str]="gelu"    ,  UpperCAmelCase						:   List[str]=False    ,  UpperCAmelCase						:   str=0.0_2    ,  UpperCAmelCase						:   str=1e-5    ,  **UpperCAmelCase						:   Tuple    ,  )    ->			int:
     super().__init__(**UpperCAmelCase			)

     lowerCamelCase__					:   Optional[Any]      				=	image_size
     lowerCamelCase__					:   Optional[int]      				=	patch_size
     lowerCamelCase__					:   int      				=	num_channels
     lowerCamelCase__					:   int      				=	embed_dim
     lowerCamelCase__					:   Optional[Any]      				=	depths
     lowerCamelCase__					:   Optional[int]      				=	len(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[int]      				=	num_heads
     lowerCamelCase__					:   Tuple      				=	window_size
     lowerCamelCase__					:   Dict      				=	mlp_ratio
     lowerCamelCase__					:   str      				=	qkv_bias
     lowerCamelCase__					:   Tuple      				=	hidden_dropout_prob
     lowerCamelCase__					:   str      				=	attention_probs_dropout_prob
     lowerCamelCase__					:   str      				=	drop_path_rate
     lowerCamelCase__					:   int      				=	hidden_act
     lowerCamelCase__					:   str      				=	use_absolute_embeddings
     lowerCamelCase__					:   List[Any]      				=	layer_norm_eps
     lowerCamelCase__					:   Optional[Any]      				=	initializer_range
     # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     lowerCamelCase__					:   Optional[int]      				=	int(embed_dim * 2 ** (len(UpperCAmelCase			) - 1)			)





