'''simple docstring'''

import logging
import random

import ray

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


__a: Tuple          =   logging.getLogger(__name__)



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self )  ->  Dict:
        lowercase__				:    List[str]  =					False

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  str:

        if not self.initialized:
         lowercase__				:    int  =					RagRetriever(
             a_	,   question_encoder_tokenizer=a_	,   generator_tokenizer=a_	,   index=a_	,   init_retrieval=a_	,   )
         lowercase__				:    str  =					True

       def _lowerCAmelCase(						self )  ->  Tuple:
        self.retriever.index.init_index()


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    List[Any]  =					self.retriever._main_retrieve(a_	,   a_ )
        return doc_ids, retrieved_doc_embeds



class 			UpperCAmelCase  (						__snake_case					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=None )  ->  Tuple:

        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_	,   )
        lowercase__				:    Tuple  =					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 _lowerCAmelCase(						self )  ->  List[Any]:
        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	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  str:

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

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase=None	,   **__lowerCAmelCase )  ->  str:
        return super(a_	,   cls ).get_tokenizers(a_	,   a_	,   **a_ )


       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=None	,   **__lowerCAmelCase )  ->  int:
        lowercase__				:    Optional[Any]  =					kwargs.pop('''config'''	,   a_ ) or RagConfig.from_pretrained(a_	,   **a_ )
        lowercase__				:    Any  =					RagTokenizer.from_pretrained(a_	,   config=a_ )
        lowercase__				:    Union[str, Any]  =					rag_tokenizer.question_encoder
        lowercase__				:    Tuple  =					rag_tokenizer.generator
        if indexed_dataset is not None:
         lowercase__				:    Union[str, Any]  =					'''custom'''
         lowercase__				:    Optional[int]  =					CustomHFIndex(config.retrieval_vector_size	,   a_ )
        else:
         lowercase__				:    Dict  =					cls._build_index(a_ )
        return cls(
            a_	,   question_encoder_tokenizer=a_	,   generator_tokenizer=a_	,   retrieval_workers=a_	,   index=a_	,   )





"""simple docstring"""
# 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


SCREAMING_SNAKE_CASE :    Union[str, Any]									=				subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
SCREAMING_SNAKE_CASE :    Any									=				subprocess.check_output(F'git diff --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()

SCREAMING_SNAKE_CASE :    Union[str, Any]									=				"""|""".join(sys.argv[1:])
SCREAMING_SNAKE_CASE :    int									=				re.compile(rF'^({joined_dirs}).*?\.py$')

SCREAMING_SNAKE_CASE :    str									=				[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")





