"""simple docstring"""




import logging
import random

import ray

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


_a      :		Any           =   logging.getLogger(__name__)
class 						__A							:






     def __init__(		self  ):
       _lowerCAmelCase						:       Tuple  			=     False






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__  ):
       if not self.initialized:
         _lowerCAmelCase						:       Any  			=     RagRetriever(
             a__				,     question_encoder_tokenizer=a__				,     generator_tokenizer=a__				,     index=a__				,     init_retrieval=a__				,     )
         _lowerCAmelCase						:       Optional[int]  			=     True






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






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     self.retriever._main_retrieve(a__				,     a__  )
       return doc_ids, retrieved_doc_embeds







class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__				,     a__				,     a__				,     a__=None  ):
       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__				,     )
       _lowerCAmelCase						:       Union[str, Any]  			=     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     __A  (		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     __A  (		self				,     a__				,     a__  ):
       if len(self.retrieval_workers  ) > 0:
         # Select a random retrieval actor.
         _lowerCAmelCase						:       List[str]  			=     self.retrieval_workers[random.randint(0				,     len(self.retrieval_workers  ) - 1  )]
         _lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     ray.get(random_worker.retrieve.remote(a__				,     a__  )  )
       else:
         _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     self._main_retrieve(a__				,     a__  )
       return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a__  )






     @classmethod
     def     __A  (		cls				,     a__				,     a__=None				,     **a__  ):
       return super(a__				,     cls  ).get_tokenizers(a__				,     a__				,     **a__  )






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











"""simple docstring"""
from manim import *






class 					_SCREAMING_SNAKE_CASE(   A  ):




       def     _UpperCamelCase     (						self							)			->      str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  Rectangle(height=0.5		,width=0.5							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  Rectangle(height=0.4_6		,width=0.4_6							).set_stroke(width=0							)

             __SCREAMING_SNAKE_CASE   :List[str]						=  [mem.copy() for i in range(6							)]
             __SCREAMING_SNAKE_CASE   :List[str]						=  [mem.copy() for i in range(6							)]
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  VGroup(*SCREAMING_SNAKE_CASE__							).arrange(SCREAMING_SNAKE_CASE__		,buff=0							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  VGroup(*SCREAMING_SNAKE_CASE__							).arrange(SCREAMING_SNAKE_CASE__		,buff=0							)
             __SCREAMING_SNAKE_CASE   :Any						=  VGroup(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							).arrange(SCREAMING_SNAKE_CASE__		,buff=0							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  Text('''CPU'''		,font_size=24							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  Group(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							).arrange(SCREAMING_SNAKE_CASE__		,buff=0.5		,aligned_edge=SCREAMING_SNAKE_CASE__							)
             cpu.move_to([-2.5, -0.5, 0]							)
             self.add(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :List[Any]						=  [mem.copy() for i in range(1							)]
             __SCREAMING_SNAKE_CASE   :str						=  VGroup(*SCREAMING_SNAKE_CASE__							).arrange(SCREAMING_SNAKE_CASE__		,buff=0							)
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  Text('''GPU'''		,font_size=24							)
             __SCREAMING_SNAKE_CASE   :int						=  Group(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							).arrange(SCREAMING_SNAKE_CASE__		,buff=0.5		,aligned_edge=SCREAMING_SNAKE_CASE__							)
             gpu.align_to(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             gpu.set_x(gpu.get_x() - 1							)

             self.add(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :str						=  [mem.copy() for i in range(6							)]
             __SCREAMING_SNAKE_CASE   :int						=  VGroup(*SCREAMING_SNAKE_CASE__							).arrange(SCREAMING_SNAKE_CASE__		,buff=0							)

             __SCREAMING_SNAKE_CASE   :List[Any]						=  Text('''Model'''		,font_size=24							)
             __SCREAMING_SNAKE_CASE   :int						=  Group(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							).arrange(SCREAMING_SNAKE_CASE__		,buff=0.5		,aligned_edge=SCREAMING_SNAKE_CASE__							)
             model.move_to([3, -1.0, 0]							)

             self.play(
                 Create(SCREAMING_SNAKE_CASE__		,run_time=1							)		,Create(SCREAMING_SNAKE_CASE__		,run_time=1							)		,Create(SCREAMING_SNAKE_CASE__		,run_time=1							)		,)

             __SCREAMING_SNAKE_CASE   :List[str]						=  MarkupText(
                 f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''		,font_size=24		,)

             __SCREAMING_SNAKE_CASE   :List[Any]						=  Square(side_length=2.2							)
             key.move_to([-5, 2, 0]							)

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  MarkupText(
                 f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''		,font_size=18		,)

             key_text.move_to([-5, 2.4, 0]							)

             step_a.move_to([2, 2, 0]							)
             self.play(Write(SCREAMING_SNAKE_CASE__		,run_time=2.5							)		,Write(SCREAMING_SNAKE_CASE__							)		,Write(SCREAMING_SNAKE_CASE__							)							)

             self.add(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  []
             __SCREAMING_SNAKE_CASE   :int						=  []
             __SCREAMING_SNAKE_CASE   :List[Any]						=  []
             for i, rect in enumerate(SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE   :Any						=  Rectangle(height=0.4_6		,width=0.4_6							).set_stroke(width=0.0							).set_fill(SCREAMING_SNAKE_CASE__		,opacity=0.7							)
                   cpu_target.move_to(SCREAMING_SNAKE_CASE__							)
                   cpu_target.generate_target()
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  0.4_6 / 4
                   __SCREAMING_SNAKE_CASE   :Tuple						=  0.4_6 / 3

                   if i == 0:
                         cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							)		,buff=0.0_2		,direction=SCREAMING_SNAKE_CASE__							)
                         cpu_target.target.set_x(cpu_target.target.get_x() + 0.1							)
                   elif i == 3:
                         cpu_target.target.next_to(cpu_targs[0].target		,direction=SCREAMING_SNAKE_CASE__		,buff=0.0							)
                   else:
                         cpu_target.target.next_to(cpu_targs[i - 1].target		,direction=SCREAMING_SNAKE_CASE__		,buff=0.0							)
                   cpu_targs.append(SCREAMING_SNAKE_CASE__							)

                   first_animations.append(rect.animate(run_time=0.5							).set_stroke(SCREAMING_SNAKE_CASE__							)							)
                   second_animations.append(MoveToTarget(SCREAMING_SNAKE_CASE__		,run_time=1.5							)							)

             self.play(*SCREAMING_SNAKE_CASE__							)
             self.play(*SCREAMING_SNAKE_CASE__							)

             self.wait()