




'''simple docstring'''


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      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  str       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  False






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  List[Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  List[str]						,						lowercase__   :  Any       ):


           '''simple docstring'''

           if not self.initialized:
                a_     :							Optional[Any]     						=  RagRetriever(
                    lowercase__						,						question_encoder_tokenizer=lowercase__						,						generator_tokenizer=lowercase__						,						index=lowercase__						,						init_retrieval=lowercase__						,						)
                a_     :							Union[str, Any]     						=  True






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           self.retriever.index.init_index()






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Dict       ):


           '''simple docstring'''

           a_						,     a_     :							Optional[int]     						=  self.retriever._main_retrieve(lowercase__						,						lowercase__       )
           return doc_ids, retrieved_doc_embeds







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def __init__( self   :  Union[str, Any]						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  List[Any]						,						lowercase__   :  List[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  List[str]=None       ):


           '''simple docstring'''

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






      def 							lowercase_						( self   :  Optional[int]       ):


           '''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 							lowercase_						( self   :  Any						,						lowercase__   :  Tuple						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

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






      @classmethod
      def 							lowercase_						( cls   :  Optional[int]						,						lowercase__   :  Optional[int]						,						lowercase__   :  int=None						,						**lowercase__   :  List[Any]       ):


           '''simple docstring'''

           return super(lowercase__						,						cls       ).get_tokenizers(lowercase__						,						lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  str						,						lowercase__   :  str						,						lowercase__   :  Dict						,						lowercase__   :  Tuple=None						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							Dict     						=  kwargs.pop("""config"""						,						lowercase__       ) or RagConfig.from_pretrained(lowercase__						,						**lowercase__       )
           a_     :							Dict     						=  RagTokenizer.from_pretrained(lowercase__						,						config=lowercase__       )
           a_     :							Optional[int]     						=  rag_tokenizer.question_encoder
           a_     :							Optional[Any]     						=  rag_tokenizer.generator
           if indexed_dataset is not None:
                a_     :							Dict     						=  """custom"""
                a_     :							List[str]     						=  CustomHFIndex(config.retrieval_vector_size						,						lowercase__       )
           else:
                a_     :							List[str]     						=  cls._build_index(lowercase__       )
           return cls(
               lowercase__						,						question_encoder_tokenizer=lowercase__						,						generator_tokenizer=lowercase__						,						retrieval_workers=lowercase__						,						index=lowercase__						,						)






'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowerCAmelCase_      :  List[Any]				     =  2_9979_2458

# Symbols
lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_      :  Tuple				     =  symbols('ct x y z')



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     if velocity > c:
          raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""				)
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError("""Speed must be greater than or equal to 1!"""				)

     return velocity / c



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return 1 / sqrt(1 - beta(UpperCamelCase__				) ** 2				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return np.array(
         [
             [gamma(UpperCamelCase__				), -gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), 0, 0],
             [-gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), gamma(UpperCamelCase__				), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					np.ndarray | None = None				):




     """simple docstring"""


     if event is None:
          a_     :							str     						=  np.array([ct, x, y, z]				)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(UpperCamelCase__				) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 lowerCAmelCase_      :  Union[str, Any]				     =  transform(2997_9245)
 print('Example of four vector: ')
 print(f"ct' = {four_vector[0]}")
 print(f"x' = {four_vector[1]}")
 print(f"y' = {four_vector[2]}")
 print(f"z' = {four_vector[3]}")

 # Substitute symbols with numerical values
 lowerCAmelCase_      :  Tuple				     =  {ct: c, x: 1, y: 1, z: 1}
 lowerCAmelCase_      :  Optional[int]				     =  [four_vector[i].subs(sub_dict) for i in range(4)]

 print(f"\n{numerical_vector}")
