


'''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_  :				Dict						= logging.get_logger(__name__)





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   lowerCamelCase_      =  nn.ModuleList(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = False			,  UpperCamelCase = True			,  ):







   """simple docstring"""
   for i, (image, scale, controlnet) in enumerate(zip(UpperCamelCase			,  UpperCamelCase			,  self.nets     )     ):
    lowerCamelCase_ ,lowerCamelCase_      =  controlnet(
        UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  )

    # merge samples
    if i == 0:
     lowerCamelCase_ ,lowerCamelCase_      =  down_samples, mid_sample
    else:
     lowerCamelCase_      =  [
         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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = True			,  UpperCamelCase = None			,  UpperCamelCase = False			,  UpperCamelCase = None			,  ):







   """simple docstring"""
   lowerCamelCase_      =  0
   lowerCamelCase_      =  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
    lowerCamelCase_      =  model_path_to_save + f'''_{idx}'''







  @classmethod
  def   snake_case		(    cls			,  UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   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(UpperCamelCase     ):
    lowerCamelCase_      =  ControlNetModel.from_pretrained(UpperCamelCase			,  **UpperCamelCase     )
    controlnets.append(UpperCamelCase     )

    idx += 1
    lowerCamelCase_      =  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 __future__ import annotations

from math import pi






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float    ,       _lowercase						:    float					) ->dict[str, float]:






	'''simple docstring'''






	if (inductance, frequency, reactance).count(0					) != 1:
		raise ValueError("One and only one argument must be 0"					)
	if inductance < 0:
		raise ValueError("Inductance cannot be negative"					)
	if frequency < 0:
		raise ValueError("Frequency cannot be negative"					)
	if reactance < 0:
		raise ValueError("Inductive reactance cannot be negative"					)
	if inductance == 0:
		return {"inductance": reactance / (2 * pi * frequency)}
	elif frequency == 0:
		return {"frequency": reactance / (2 * pi * inductance)}
	elif reactance == 0:
		return {"reactance": 2 * pi * frequency * inductance}
	else:
		raise ValueError("Exactly one argument must be 0"					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()


