
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


lowerCamelCase_						     =					logging.get_logger(__name__)




class      __A(    _UpperCAmelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_						):
       super().__init__()
       UpperCamelCase__				=    nn.ModuleList(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = True	,      ):
       for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      self.nets						)						):
            UpperCamelCase__				=    controlnet(
                SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      )

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

       return down_block_res_samples, mid_block_res_sample







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = True	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = None	,      ):
       UpperCamelCase__				=    0
       UpperCamelCase__				=    save_directory
       for controlnet in self.nets:
            controlnet.save_pretrained(
                SCREAMING_SNAKE_CASE_	,      is_main_process=SCREAMING_SNAKE_CASE_	,      save_function=SCREAMING_SNAKE_CASE_	,      safe_serialization=SCREAMING_SNAKE_CASE_	,      variant=SCREAMING_SNAKE_CASE_	,      )

            idx += 1
            UpperCamelCase__				=    model_path_to_save + F"_{idx}"







  @classmethod
  def 	UpperCAmelCase_ (cls	,      SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       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(SCREAMING_SNAKE_CASE_						):
            UpperCamelCase__				=    ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)
            controlnets.append(SCREAMING_SNAKE_CASE_						)

            idx += 1
            UpperCamelCase__				=    pretrained_model_path + F"_{idx}"

       logger.info(F"{len(SCREAMING_SNAKE_CASE_						)} controlnets loaded from {pretrained_model_path}."						)

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

       return cls(SCREAMING_SNAKE_CASE_						)







import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


lowerCamelCase_						     =					logging.get_logger(__name__)




class      __A(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       warnings.warn(
           """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
           """ Please use SegformerImageProcessor instead."""	,      SCREAMING_SNAKE_CASE_	,      )
       super().__init__(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)





