






'''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


__A    	=   logging.get_logger(__name__)




class a_    (    lowercase__							):







   def __init__(self					,   __a)			->      Dict:
      """simple docstring"""






      super().__init__()
      __snake_case				:      List[str]     							= nn.ModuleList(__lowerCamelCase)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a					,   __a					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = False					,   __a = True					,   )			->      Union[str, Any]:
      """simple docstring"""






      for i, (image, scale, controlnet) in enumerate(zip(__lowerCamelCase					,   __lowerCamelCase					,   self.nets)):
         __snake_case				:      int     							= controlnet(
             __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   )

         # merge samples
         if i == 0:
            __snake_case				:      Union[str, Any]     							= down_samples, mid_sample
         else:
            __snake_case				:      Tuple     							= [
                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     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = True					,   __a = None					,   __a = False					,   __a = None					,   )			->      List[str]:
      """simple docstring"""






      __snake_case				:      Any     							= 0
      __snake_case				:      Tuple     							= 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
         __snake_case				:      Optional[Any]     							= model_path_to_save + F"""_{idx}"""







   @classmethod
   def     SCREAMING_SNAKE_CASE__					(cls					,   __a					,   **__a)			->      Any:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= 0
      __snake_case				:      List[str]     							= []

      # 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`, ...
      __snake_case				:      int     							= pretrained_model_path
      while os.path.isdir(__lowerCamelCase):
         __snake_case				:      int     							= ControlNetModel.from_pretrained(__lowerCamelCase					,   **__lowerCamelCase)
         controlnets.append(__lowerCamelCase)

         idx += 1
         __snake_case				:      int     							= 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A    	=   {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BioGptForCausalLM''',
          '''BioGptForTokenClassification''',
          '''BioGptForSequenceClassification''',
          '''BioGptModel''',
          '''BioGptPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)