






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


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''van'''







      def __init__(      self      :						List[str] ,					__UpperCAmelCase      :						int=224 ,					__UpperCAmelCase      :						Tuple=3 ,					__UpperCAmelCase      :						Optional[Any]=[7, 3, 3, 3] ,					__UpperCAmelCase      :						Tuple=[4, 2, 2, 2] ,					__UpperCAmelCase      :						Optional[Any]=[64, 128, 320, 512] ,					__UpperCAmelCase      :						List[str]=[3, 3, 12, 3] ,					__UpperCAmelCase      :						Optional[int]=[8, 8, 4, 4] ,					__UpperCAmelCase      :						Optional[int]="gelu" ,					__UpperCAmelCase      :						Optional[Any]=0.02 ,					__UpperCAmelCase      :						Optional[Any]=1e-6 ,					__UpperCAmelCase      :						str=1e-2 ,					__UpperCAmelCase      :						Dict=0.0 ,					__UpperCAmelCase      :						Optional[Any]=0.0 ,					**__UpperCAmelCase      :						List[Any] ,					)    ->Union[str, Any]:




       """simple docstring"""


       super().__init__(**__UpperCAmelCase  )
       a		  =      image_size
       a		  =      num_channels
       a		  =      patch_sizes
       a		  =      strides
       a		  =      hidden_sizes
       a		  =      depths
       a		  =      mlp_ratios
       a		  =      hidden_act
       a		  =      initializer_range
       a		  =      layer_norm_eps
       a		  =      layer_scale_init_value
       a		  =      drop_path_rate
       a		  =      dropout_rate







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     ,		)





