




import logging
import random

import ray

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


__lowerCAmelCase	:       List[str]     =	logging.getLogger(__name__)




class 					snake_case__							:
    """simple docstring"""



    def __init__(   self       :  Dict							)      ->  Tuple:
       a					=    False





    def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  Tuple:
       if not self.initialized:
          a					=    RagRetriever(
              A_      ,   question_encoder_tokenizer=A_      ,   generator_tokenizer=A_      ,   index=A_      ,   init_retrieval=A_      ,   )
          a					=    True





    def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  str:
       self.retriever.index.init_index()





    def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  int							)      ->  List[Any]:
       a					,	a					=    self.retriever._main_retrieve(A_      ,   A_							)
       return doc_ids, retrieved_doc_embeds




class 					snake_case__							(_lowercase     ):
    """simple docstring"""



    def __init__(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  str=None							)      ->  List[str]:
       if index is not None and index.is_initialized() and len(A_							) > 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__(
           A_      ,   question_encoder_tokenizer=A_      ,   generator_tokenizer=A_      ,   index=A_      ,   init_retrieval=A_      ,   )
       a					=    retrieval_workers
       if len(self.retrieval_workers							) > 0:
          ray.get(
              [
                  worker.create_rag_retriever.remote(A_      ,   A_      ,   A_      ,   A_							)
                  for worker in self.retrieval_workers
              ]							)





    def 							__UpperCAmelCase							(   self       :  str							)      ->  int:
       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 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  int							)      ->  Optional[Any]:
       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(A_      ,   A_							)							)
       else:
          a					,	a					=    self._main_retrieve(A_      ,   A_							)
       return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A_							)





    @classmethod
    def 							__UpperCAmelCase							(   cls       :  Any      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Optional[int]=None      ,   **__lowerCamelCase       :  Any							)      ->  str:
       return super(A_      ,   cls							).get_tokenizers(A_      ,   A_      ,   **A_							)





    @classmethod
    def 							__UpperCAmelCase							(   cls       :  Any      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  int=None      ,   **__lowerCamelCase       :  List[str]							)      ->  Union[str, Any]:
       a					=    kwargs.pop("config"      ,   A_							) or RagConfig.from_pretrained(A_      ,   **A_							)
       a					=    RagTokenizer.from_pretrained(A_      ,   config=A_							)
       a					=    rag_tokenizer.question_encoder
       a					=    rag_tokenizer.generator
       if indexed_dataset is not None:
          a					=    "custom"
          a					=    CustomHFIndex(config.retrieval_vector_size      ,   A_							)
       else:
          a					=    cls._build_index(A_							)
       return cls(
           A_      ,   question_encoder_tokenizer=A_      ,   generator_tokenizer=A_      ,   retrieval_workers=A_      ,   index=A_      ,   )












# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase	:       Tuple     =	subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
__lowerCAmelCase	:       Tuple     =	subprocess.check_output(F'''git diff --name-only {fork_point_sha}'''.split()).decode('utf-8').split()

__lowerCAmelCase	:       Dict     =	'|'.join(sys.argv[1:])
__lowerCAmelCase	:       List[Any]     =	re.compile(rF'''^({joined_dirs}).*?\.py$''')

__lowerCAmelCase	:       List[Any]     =	[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')






