'''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


lowercase__  :      Union[str, Any] =					logging.get_logger(__name__)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def __init__(      self			:       Tuple  ,							UpperCAmelCase__			:       Union[List[ControlNetModel], Tuple[ControlNetModel]]  )				->Any:
        super().__init__()
        UpperCAmelCase_            =  nn.ModuleList(UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       torch.FloatTensor  ,							UpperCAmelCase__			:       Union[torch.Tensor, float, int]  ,							UpperCAmelCase__			:       torch.Tensor  ,							UpperCAmelCase__			:       List[torch.tensor]  ,							UpperCAmelCase__			:       List[float]  ,							UpperCAmelCase__			:       Optional[torch.Tensor] = None  ,							UpperCAmelCase__			:       Optional[torch.Tensor] = None  ,							UpperCAmelCase__			:       Optional[torch.Tensor] = None  ,							UpperCAmelCase__			:       Optional[Dict[str, Any]] = None  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       bool = True  ,							)				->Union[ControlNetOutput, Tuple]:
        for i, (image, scale, controlnet) in enumerate(zip(UpperCAmelCase__  ,							UpperCAmelCase__  ,							self.nets  )  ):
              UpperCAmelCase_      ,		UpperCAmelCase_            =  controlnet(
                  UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							)

              # merge samples
              if i == 0:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  down_samples, mid_sample
              else:
                    UpperCAmelCase_            =  [
                        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			:       Dict  ,							UpperCAmelCase__			:       Union[str, os.PathLike]  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Callable = None  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       Optional[str] = None  ,							)				->Union[str, Any]:
        UpperCAmelCase_            =  0
        UpperCAmelCase_            =  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
              UpperCAmelCase_            =  model_path_to_save + f"""_{idx}"""






  @classmethod
  def  lowerCAmelCase__ (      cls			:       Any  ,							UpperCAmelCase__			:       Optional[Union[str, os.PathLike]]  ,							**UpperCAmelCase__			:       Any  )				->List[str]:
        UpperCAmelCase_            =  0
        UpperCAmelCase_            =  []

        # 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`, ...
        UpperCAmelCase_            =  pretrained_model_path
        while os.path.isdir(UpperCAmelCase__  ):
              UpperCAmelCase_            =  ControlNetModel.from_pretrained(UpperCAmelCase__  ,							**UpperCAmelCase__  )
              controlnets.append(UpperCAmelCase__  )

              idx += 1
              UpperCAmelCase_            =  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_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__  :      Union[str, Any]        =					{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Union[str, Any]        =					["MobileViTFeatureExtractor"]
      lowercase__  :      List[Any]        =					["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Dict        =					[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__  :      Optional[int]        =					[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      lowercase__  :      int        =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





