'''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


__UpperCAmelCase     							=logging.get_logger(__name__)




class     a__      (   UpperCAmelCase__ ):






     def __init__( self		:		Optional[Any]  ,				a		:		Union[List[ControlNetModel], Tuple[ControlNetModel]]    ):





         """simple docstring"""



         super().__init__()
         __lowerCamelCase		=  nn.ModuleList(a    )






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





         """simple docstring"""



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

             # merge samples
             if i == 0:
                 __lowerCamelCase	,  __lowerCamelCase		=  down_samples, mid_sample
             else:
                 __lowerCamelCase		=  [
                     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    SCREAMING_SNAKE_CASE__   ( self		:		Any  ,				a		:		Union[str, os.PathLike]  ,				a		:		bool = True  ,				a		:		Callable = None  ,				a		:		bool = False  ,				a		:		Optional[str] = None  ,				):





         """simple docstring"""



         __lowerCamelCase		=  0
         __lowerCamelCase		=  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
             __lowerCamelCase		=  model_path_to_save + f"""_{idx}"""






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





         """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(a    ):
             __lowerCamelCase		=  ControlNetModel.from_pretrained(a  ,				**a    )
             controlnets.append(a    )

             idx += 1
             __lowerCamelCase		=  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    )





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__snake_case						:List[Any]      		=     logging.getLogger(__name__)

class     _A       :



       def __init__(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				False



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           if not self.initialized:
               __a						       =				RagRetriever(
                   __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
               __a						       =				True



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           self.retriever.index.init_index()





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return doc_ids, retrieved_doc_embeds


class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None):


           '''simple docstring'''

           if index is not None and index.is_initialized() and len(__SCREAMING_SNAKE_CASE) > 0:
               raise ValueError(
                   '''When using Ray for distributed fine-tuning, '''
                   '''you\'ll need to provide the paths instead, '''
                   '''as the dataset and the index are loaded '''
                   '''separately. More info in examples/rag/use_own_knowledge_dataset.py ''')
           super().__init__(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		init_retrieval=__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				retrieval_workers
           if len(self.retrieval_workers) > 0:
               ray.get(
                   [
                       worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
                       for worker in self.retrieval_workers
                   ])



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           logger.info('''initializing retrieval''')

           if len(self.retrieval_workers) > 0:
               ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
           else:
               # Non-distributed training. Load index into this same process.
               self.index.init_index()



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           if len(self.retrieval_workers) > 0:
               # Select a random retrieval actor.
               __a						       =				self.retrieval_workers[random.randint(0     ,		len(self.retrieval_workers) - 1)]
               __a       ,   __a						       =				ray.get(random_worker.retrieve.remote(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE))
           else:
               __a       ,   __a						       =				self._main_retrieve(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__SCREAMING_SNAKE_CASE)



       @classmethod
       def 		_lowerCamelCase			(		cls							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		**__SCREAMING_SNAKE_CASE							:     Optional[int]):


           '''simple docstring'''

           return super(__SCREAMING_SNAKE_CASE     ,		cls).get_tokenizers(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)





       @classmethod
       def 		_lowerCamelCase			(		cls							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     str=None     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				kwargs.pop('''config'''     ,		__SCREAMING_SNAKE_CASE) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     ,		config=__SCREAMING_SNAKE_CASE)
           __a						       =				rag_tokenizer.question_encoder
           __a						       =				rag_tokenizer.generator
           if indexed_dataset is not None:
               __a						       =				'''custom'''
               __a						       =				CustomHFIndex(config.retrieval_vector_size     ,		__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				cls._build_index(__SCREAMING_SNAKE_CASE)
           return cls(
               __SCREAMING_SNAKE_CASE     ,		question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,		generator_tokenizer=__SCREAMING_SNAKE_CASE     ,		retrieval_workers=__SCREAMING_SNAKE_CASE     ,		index=__SCREAMING_SNAKE_CASE     ,		)





