


"""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


__snake_case             =   logging.get_logger(__name__)







class _lowerCAmelCase    (				snake_case_     ):







			def __init__(     self						,     UpperCamelCase__						)     ->		Dict:

							'''simple docstring'''




							super().__init__()
							snake_case   :				Tuple				 =   nn.ModuleList(UpperCamelCase__						)







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = True						,     )     ->		Union[ControlNetOutput, Tuple]:

							'''simple docstring'''




							for i, (image, scale, controlnet) in enumerate(zip(UpperCamelCase__						,     UpperCamelCase__						,     self.nets						)						):
											snake_case   :				Optional[Any]				 =   controlnet(
											    UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     )

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

							return down_block_res_samples, mid_block_res_sample







			def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     )     ->		Any:

							'''simple docstring'''




							snake_case   :				int				 =   0
							snake_case   :				List[Any]				 =   save_directory
							for controlnet in self.nets:
											controlnet.save_pretrained(
											    UpperCamelCase__						,     is_main_process=UpperCamelCase__						,     save_function=UpperCamelCase__						,     safe_serialization=UpperCamelCase__						,     variant=UpperCamelCase__						,     )

											idx += 1
											snake_case   :				List[str]				 =   model_path_to_save + F'_{idx}'







			@classmethod
			def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		List[str]:

							'''simple docstring'''




							snake_case   :				List[str]				 =   0
							snake_case   :				int				 =   []

							# 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   :				Optional[Any]				 =   pretrained_model_path
							while os.path.isdir(UpperCamelCase__						):
											snake_case   :				int				 =   ControlNetModel.from_pretrained(UpperCamelCase__						,     **UpperCamelCase__						)
											controlnets.append(UpperCamelCase__						)

											idx += 1
											snake_case   :				Dict				 =   pretrained_model_path + F'_{idx}'

							logger.info(F'{len(UpperCamelCase__						)} controlnets loaded from {pretrained_model_path}.'						)

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

							return cls(UpperCamelCase__						)









"""simple docstring"""



from typing import TYPE_CHECKING

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


__snake_case             =   {
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   ["""ConditionalDetrFeatureExtractor"""]
  __snake_case             =   ["""ConditionalDetrImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   [
      """CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConditionalDetrForObjectDetection""",
      """ConditionalDetrForSegmentation""",
      """ConditionalDetrModel""",
      """ConditionalDetrPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConditionalDetrConfig,
      ConditionalDetrOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
    from .image_processing_conditional_detr import ConditionalDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_conditional_detr import (
        CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConditionalDetrForObjectDetection,
        ConditionalDetrForSegmentation,
        ConditionalDetrModel,
        ConditionalDetrPreTrainedModel,
    )

else:
  import sys

  __snake_case             =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





