





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase							:					Optional[int]		    =		logging.get_logger(__name__)

_lowerCAmelCase							:					int		    =		{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}


class     __magic_name__     (							lowerCamelCase__							):





     """simple docstring"""



     __UpperCamelCase    			=   "distilbert"
     __UpperCamelCase    			=   {
         "hidden_size": "dim",
         "num_attention_heads": "n_heads",
         "num_hidden_layers": "n_layers",
     }



     def __init__(							self				:Union[str, Any]	,					snake_case				:List[Any]=30_522	,					snake_case				:Dict=512	,					snake_case				:int=False	,					snake_case				:int=6	,					snake_case				:Union[str, Any]=12	,					snake_case				:Union[str, Any]=768	,					snake_case				:Union[str, Any]=4 * 768	,					snake_case				:Dict=0.1	,					snake_case				:List[Any]=0.1	,					snake_case				:Dict="gelu"	,					snake_case				:Tuple=0.02	,					snake_case				:str=0.1	,					snake_case				:str=0.2	,					snake_case				:List[str]=0	,					**snake_case				:Tuple	,					):





        '''simple docstring'''




        A_    : str								=	vocab_size
        A_    : Optional[Any]								=	max_position_embeddings
        A_    : Any								=	sinusoidal_pos_embds
        A_    : List[str]								=	n_layers
        A_    : Dict								=	n_heads
        A_    : Any								=	dim
        A_    : Union[str, Any]								=	hidden_dim
        A_    : Union[str, Any]								=	dropout
        A_    : Tuple								=	attention_dropout
        A_    : Optional[Any]								=	activation
        A_    : List[Any]								=	initializer_range
        A_    : List[str]								=	qa_dropout
        A_    : Union[str, Any]								=	seq_classif_dropout
        super().__init__(**snake_case	,					pad_token_id=snake_case     )




class     __magic_name__     (							lowerCamelCase__							):





     """simple docstring"""



     @property
     def   SCREAMING_SNAKE_CASE						(							self				:Tuple     ):





        '''simple docstring'''




        if self.task == "multiple-choice":
           A_    : Dict								=	{0: "batch", 1: "choice", 2: "sequence"}
        else:
           A_    : Union[str, Any]								=	{0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
            ]     )


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       :				List[str]       =			logging.get_logger(__name__)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



	def __init__(  self	:    Dict			,  lowercase	:    Union[List[ControlNetModel], Tuple[ControlNetModel]]	):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(lowercase	)



	def  A							(  self	:    Optional[int]			,  lowercase	:    torch.FloatTensor			,  lowercase	:    Union[torch.Tensor, float, int]			,  lowercase	:    torch.Tensor			,  lowercase	:    List[torch.tensor]			,  lowercase	:    List[float]			,  lowercase	:    Optional[torch.Tensor] = None			,  lowercase	:    Optional[torch.Tensor] = None			,  lowercase	:    Optional[torch.Tensor] = None			,  lowercase	:    Optional[Dict[str, Any]] = None			,  lowercase	:    bool = False			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				for i, (image, scale, controlnet) in enumerate(zip(lowercase			,  lowercase			,  self.nets	)	):
							_snake_case	,					_snake_case		=			controlnet(
							    lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase			,  )

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

				return down_block_res_samples, mid_block_res_sample



	def  A							(  self	:    Dict			,  lowercase	:    Union[str, os.PathLike]			,  lowercase	:    bool = True			,  lowercase	:    Callable = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[str] = None			,  ):


				'''simple docstring'''


				_snake_case		=			0
				_snake_case		=			save_directory
				for controlnet in self.nets:
							controlnet.save_pretrained(
							    lowercase			,  is_main_process=lowercase			,  save_function=lowercase			,  safe_serialization=lowercase			,  variant=lowercase			,  )

							idx += 1
							_snake_case		=			model_path_to_save + f'''_{idx}'''





	@classmethod
	def  A							(  cls	:    Any			,  lowercase	:    Optional[Union[str, os.PathLike]]			,  **lowercase	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			0
				_snake_case		=			[]

				# 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		=			pretrained_model_path
				while os.path.isdir(lowercase	):
							_snake_case		=			ControlNetModel.from_pretrained(lowercase			,  **lowercase	)
							controlnets.append(lowercase	)

							idx += 1
							_snake_case		=			pretrained_model_path + f'''_{idx}'''

				logger.info(f'''{len(lowercase	)} controlnets loaded from {pretrained_model_path}.'''	)

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

				return cls(lowercase	)