





'''simple docstring'''


import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase		)     ->				List[Any]:
     super().__init__()
     _lowerCAmelCase						      =   nn.ModuleList(_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = True      ,  )     ->				Union[ControlNetOutput, Tuple]:
     for i, (image, scale, controlnet) in enumerate(zip(_lowerCAmelCase      ,  _lowerCAmelCase      ,  self.nets		)		):
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   controlnet(
             _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  )

         # merge samples
         if i == 0:
             _lowerCAmelCase      ,      _lowerCAmelCase						      =   down_samples, mid_sample
         else:
             _lowerCAmelCase						      =   [
                 samples_prev + samples_curr
                 for samples_prev, samples_curr in zip(_lowerCAmelCase      ,  _lowerCAmelCase		)
             ]
             mid_block_res_sample += mid_sample

     return down_block_res_samples, mid_block_res_sample




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Tuple:
     _lowerCAmelCase						      =   0
     _lowerCAmelCase						      =   save_directory
     for controlnet in self.nets:
         controlnet.save_pretrained(
             _lowerCAmelCase      ,  is_main_process=_lowerCAmelCase      ,  save_function=_lowerCAmelCase      ,  safe_serialization=_lowerCAmelCase      ,  variant=_lowerCAmelCase      ,  )

         idx += 1
         _lowerCAmelCase						      =   model_path_to_save + f'''_{idx}'''





 @classmethod
 def 				_snake_case (						cls      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   0
     _lowerCAmelCase						      =   []

     # load controlnet and append to list until no controlnet directory exists anymore
     # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
     # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
     _lowerCAmelCase						      =   pretrained_model_path
     while os.path.isdir(_lowerCAmelCase		):
         _lowerCAmelCase						      =   ControlNetModel.from_pretrained(_lowerCAmelCase      ,  **_lowerCAmelCase		)
         controlnets.append(_lowerCAmelCase		)

         idx += 1
         _lowerCAmelCase						      =   pretrained_model_path + f'''_{idx}'''

     logger.info(f'''{len(_lowerCAmelCase		)} controlnets loaded from {pretrained_model_path}.'''		)

     if len(_lowerCAmelCase		) == 0:
         raise ValueError(
             f'''No ControlNets found under {os.path.dirname(_lowerCAmelCase		)}. Expected at least {pretrained_model_path + '_0'}.'''		)

     return cls(_lowerCAmelCase		)









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "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_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "donut-swin"

 __lowerCamelCase    :				int								=       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }




 def __init__(						self      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=96      ,  _lowerCAmelCase=[2, 2, 6, 2]      ,  _lowerCAmelCase=[3, 6, 12, 24]      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=4.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  **_lowerCAmelCase      ,  )     ->				Optional[Any]:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   embed_dim
     _lowerCAmelCase						      =   depths
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)
     _lowerCAmelCase						      =   num_heads
     _lowerCAmelCase						      =   window_size
     _lowerCAmelCase						      =   mlp_ratio
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   drop_path_rate
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   use_absolute_embeddings
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   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						      =   int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)


