





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:int						    =   ['image_processor', 'tokenizer']
      _UpperCAmelCase	:Optional[Any]						    =   'CLIPImageProcessor'
      _UpperCAmelCase	:List[str]						    =   ('CLIPTokenizer', 'CLIPTokenizerFast')





      def __init__( self       ,							A_=None       ,							A_=None       ,							**A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   None
       if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."       ,							A_       ,							)
        UpperCamelCase	:					int       	=   kwargs.pop("feature_extractor"   )

       UpperCamelCase	:					Optional[int]       	=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
        raise ValueError("You need to specify an `image_processor`."   )
       if tokenizer is None:
        raise ValueError("You need to specify a `tokenizer`."   )

       super().__init__(A_       ,							A_   )





      def __call__( self       ,							A_=None       ,							A_=None       ,							A_=None       ,							**A_   ):
       '''simple docstring'''




       if text is None and images is None:
        raise ValueError("You have to specify either text or images. Both cannot be none."   )

       if text is not None:
        UpperCamelCase	:					int       	=   self.tokenizer(A_       ,							return_tensors=A_       ,							**A_   )

       if images is not None:
        UpperCamelCase	:					int       	=   self.image_processor(A_       ,							return_tensors=A_       ,							**A_   )

       if text is not None and images is not None:
        UpperCamelCase	:					Optional[Any]       	=   image_features.pixel_values
        return encoding
       elif text is not None:
        return encoding
       else:
        return BatchEncoding(data=dict(**A_   )       ,							tensor_type=A_   )





      def        __UpperCamelCase( self       ,							*A_       ,							**A_   ):
       '''simple docstring'''




       return self.tokenizer.batch_decode(*A_       ,							**A_   )





      def        __UpperCamelCase( self       ,							*A_       ,							**A_   ):
       '''simple docstring'''




       return self.tokenizer.decode(*A_       ,							**A_   )





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.tokenizer.model_input_names
       UpperCamelCase	:					int       	=   self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,							A_       ,							)
       return self.image_processor_class





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,							A_       ,							)
       return self.image_processor


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	)