


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 							lowerCamelCase   (  SCREAMING_SNAKE_CASE_ ):


	def __init__(    self				,     lowercase__):
							super().__init__()
							__UpperCAmelCase			:							Dict	    =	nn.ModuleList(__a)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = False				,     lowercase__ = True				,     ):
							for i, (image, scale, controlnet) in enumerate(zip(__a				,     __a				,     self.nets)):
													__UpperCAmelCase			:							Optional[int]	    =	controlnet(
													    __a				,     __a				,     __a				,     __a				,     __a				,     __a				,     __a				,     __a				,     __a				,     __a				,     __a				,     )

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

							return down_block_res_samples, mid_block_res_sample


	def  A(    self				,     lowercase__				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = False				,     lowercase__ = None				,     ):
							__UpperCAmelCase			:							str	    =	0
							__UpperCAmelCase			:							int	    =	save_directory
							for controlnet in self.nets:
													controlnet.save_pretrained(
													    __a				,     is_main_process=__a				,     save_function=__a				,     safe_serialization=__a				,     variant=__a				,     )

													idx += 1
													__UpperCAmelCase			:							str	    =	model_path_to_save + F"_{idx}"


	@classmethod
	def  A(    cls				,     lowercase__				,     **lowercase__):
							__UpperCAmelCase			:							List[str]	    =	0
							__UpperCAmelCase			:							Any	    =	[]

							# 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			:							Optional[Any]	    =	pretrained_model_path
							while os.path.isdir(__a):
													__UpperCAmelCase			:							Union[str, Any]	    =	ControlNetModel.from_pretrained(__a				,     **__a)
													controlnets.append(__a)

													idx += 1
													__UpperCAmelCase			:							str	    =	pretrained_model_path + F"_{idx}"

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

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

							return cls(__a)






'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__    :   Optional[Any]           =					logging.get_logger(__name__)

A__    :   List[str]           =					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= '''cvt'''
 def __init__(   self						:							Optional[Any]      ,     __a						:							Union[str, Any]=3      ,     __a						:							List[Any]=[7, 3, 3]      ,     __a						:							Optional[int]=[4, 2, 2]      ,     __a						:							Dict=[2, 1, 1]      ,     __a						:							Union[str, Any]=[64, 192, 384]      ,     __a						:							int=[1, 3, 6]      ,     __a						:							List[str]=[1, 2, 10]      ,     __a						:							Optional[Any]=[4.0, 4.0, 4.0]      ,     __a						:							Any=[0.0, 0.0, 0.0]      ,     __a						:							List[str]=[0.0, 0.0, 0.0]      ,     __a						:							List[Any]=[0.0, 0.0, 0.1]      ,     __a						:							List[str]=[True, True, True]      ,     __a						:							int=[False, False, True]      ,     __a						:							Dict=["dw_bn", "dw_bn", "dw_bn"]      ,     __a						:							List[str]=[3, 3, 3]      ,     __a						:							Union[str, Any]=[1, 1, 1]      ,     __a						:							Optional[int]=[2, 2, 2]      ,     __a						:							Optional[Any]=[1, 1, 1]      ,     __a						:							List[str]=[1, 1, 1]      ,     __a						:							List[str]=0.0_2      ,     __a						:							List[str]=1e-12      ,     **__a						:							List[str]      ,     )      ->   Dict:







       '''simple docstring'''
       super().__init__(**__a  )
       __snake_case :   int													=    num_channels
       __snake_case :   Union[str, Any]													=    patch_sizes
       __snake_case :   Any													=    patch_stride
       __snake_case :   List[str]													=    patch_padding
       __snake_case :   Optional[Any]													=    embed_dim
       __snake_case :   Union[str, Any]													=    num_heads
       __snake_case :   Dict													=    depth
       __snake_case :   Optional[Any]													=    mlp_ratio
       __snake_case :   List[str]													=    attention_drop_rate
       __snake_case :   Optional[int]													=    drop_rate
       __snake_case :   Optional[int]													=    drop_path_rate
       __snake_case :   Any													=    qkv_bias
       __snake_case :   int													=    cls_token
       __snake_case :   Optional[int]													=    qkv_projection_method
       __snake_case :   List[Any]													=    kernel_qkv
       __snake_case :   List[Any]													=    padding_kv
       __snake_case :   int													=    stride_kv
       __snake_case :   List[str]													=    padding_q
       __snake_case :   Dict													=    stride_q
       __snake_case :   Tuple													=    initializer_range
       __snake_case :   List[Any]													=    layer_norm_eps



