





"""simple docstring"""





_UpperCamelCase					      =   'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				List[str] ):


							'''simple docstring'''







							if not isinstance(_snake_case     ,    _snake_case ):
														__lowerCamelCase   :   Tuple       =F'a bytes-like object is required, not \'{data.__class__.__name__}\''
														raise TypeError(_snake_case )

							__lowerCamelCase   :   int       =''.join(bin(_snake_case )[2:].zfill(8 ) for byte in data )

							__lowerCamelCase   :   Optional[int]       =len(_snake_case ) % 6 != 0

							if padding_needed:
														# The padding that will be added later
														__lowerCamelCase   :   Any       =b'=' * ((6 - len(_snake_case ) % 6) // 2)

														# Append binary_stream with arbitrary binary digits (0's by default) to make its
														# length a multiple of 6.
														binary_stream += "0" * (6 - len(_snake_case ) % 6)
							else:
														__lowerCamelCase   :   List[str]       =b''

							# Encode every 6 binary digits to their corresponding Base64 character
							return (
							    "".join(
							        B64_CHARSET[int(binary_stream[index : index + 6]     ,    2 )]
							        for index in range(0     ,    len(_snake_case )     ,    6 ) ).encode()
							    + padding
							)



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if not isinstance(_snake_case     ,    _snake_case ) and not isinstance(_snake_case     ,    _snake_case ):
														__lowerCamelCase   :   List[str]       =(
														    'argument should be a bytes-like object or ASCII string, '
														    F'not \'{encoded_data.__class__.__name__}\''
														)
														raise TypeError(_snake_case )

							# In case encoded_data is a bytes-like object, make sure it contains only
							# ASCII characters so we convert it to a string object
							if isinstance(_snake_case     ,    _snake_case ):
														try:
																					__lowerCamelCase   :   Tuple       =encoded_data.decode('''utf-8''' )
														except UnicodeDecodeError:
																					raise ValueError('''base64 encoded data should only contain ASCII characters''' )

							__lowerCamelCase   :   Dict       =encoded_data.count('''=''' )

							# Check if the encoded string contains non base64 characters
							if padding:
														assert all(
														    char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
							else:
														assert all(
														    char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."

							# Check the padding
							assert len(_snake_case ) % 4 == 0 and padding < 3, "Incorrect padding"

							if padding:
														# Remove padding if there is one
														__lowerCamelCase   :   Tuple       =encoded_data[:-padding]

														__lowerCamelCase   :   List[str]       =''.join(
														    bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
							else:
														__lowerCamelCase   :   Tuple       =''.join(
														    bin(B64_CHARSET.index(_snake_case ) )[2:].zfill(6 ) for char in encoded_data )

							__lowerCamelCase   :   Tuple       =[
							    int(binary_stream[index : index + 8]     ,    2 )
							    for index in range(0     ,    len(_snake_case )     ,    8 )
							]

							return bytes(_snake_case )


if __name__ == "__main__":
							import doctest

							doctest.testmod()






"""simple docstring"""



import logging
import random

import ray

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


UpperCamelCase__          =	logging.getLogger(__name__)





class 				a			:



      def __init__(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					False



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if not self.initialized:
              UpperCAmelCase__	:      Optional[Any]     							=					RagRetriever(
                  UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	index=UpperCamelCase_     ,	init_retrieval=UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Union[str, Any]     							=					True



      def  __snake_case							(   self      ):
          self.retriever.index.init_index()



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					self.retriever._main_retrieve(UpperCamelCase_     ,	UpperCamelCase_      )
          return doc_ids, retrieved_doc_embeds





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          if index is not None and index.is_initialized() and len(UpperCamelCase_      ) > 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__(
              UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	index=UpperCamelCase_     ,	init_retrieval=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      Union[str, Any]     							=					retrieval_workers
          if len(self.retrieval_workers      ) > 0:
              ray.get(
                  [
                      worker.create_rag_retriever.remote(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      )
                      for worker in self.retrieval_workers
                  ]      )



      def  __snake_case							(   self      ):
          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  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if len(self.retrieval_workers      ) > 0:
              # Select a random retrieval actor.
              UpperCAmelCase__	:      Optional[Any]     							=					self.retrieval_workers[random.randint(0     ,	len(self.retrieval_workers      ) - 1      )]
              UpperCAmelCase__							,							UpperCAmelCase__	:      List[Any]     							=					ray.get(random_worker.retrieve.remote(UpperCamelCase_     ,	UpperCamelCase_      )      )
          else:
              UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					self._main_retrieve(UpperCamelCase_     ,	UpperCamelCase_      )
          return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_      )



      @classmethod
      def  __snake_case							(   cls     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          return super(UpperCamelCase_     ,	cls      ).get_tokenizers(UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )



      @classmethod
      def  __snake_case							(   cls     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					kwargs.pop('config'     ,	UpperCamelCase_      ) or RagConfig.from_pretrained(UpperCamelCase_     ,	**UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					RagTokenizer.from_pretrained(UpperCamelCase_     ,	config=UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					rag_tokenizer.question_encoder
          UpperCAmelCase__	:      Optional[Any]     							=					rag_tokenizer.generator
          if indexed_dataset is not None:
              UpperCAmelCase__	:      List[str]     							=					'custom'
              UpperCAmelCase__	:      Tuple     							=					CustomHFIndex(config.retrieval_vector_size     ,	UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[Any]     							=					cls._build_index(UpperCamelCase_      )
          return cls(
              UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	retrieval_workers=UpperCamelCase_     ,	index=UpperCamelCase_     ,	)

