




import logging
import random

import ray

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


UpperCAmelCase__    :							Any       		=     logging.getLogger(__name__)

class   UpperCAmelCase    :






   def __init__(						self :							List[str]  )->      Optional[Any]:
     _lowercase:			str      =     False






   def 			_a    (						self :							Union[str, Any]    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							Optional[int]    ,       UpperCamelCase_ :							Optional[Any]    ,       UpperCamelCase_ :							Union[str, Any]  )->      Optional[Any]:
     if not self.initialized:
       _lowercase:			int      =     RagRetriever(
           lowercase__    ,       question_encoder_tokenizer=lowercase__    ,       generator_tokenizer=lowercase__    ,       index=lowercase__    ,       init_retrieval=lowercase__    ,       )
       _lowercase:			Any      =     True






   def 			_a    (						self :							List[Any]  )->      Any:
     self.retriever.index.init_index()






   def 			_a    (						self :							List[Any]  )->      Any:
     # delete the old self.retriever object before assigning the new index
     del self.retriever
     _lowercase:			str      =     False






   def 			_a    (						self :							Optional[Any]    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							Dict  )->      str:
     _lowercase  ,       _lowercase:			List[str]      =     self.retriever._main_retrieve(lowercase__    ,       lowercase__  )
     _lowercase:			List[str]      =     self.retriever.index.get_doc_dicts(lowercase__  )
     return doc_ids, retrieved_doc_embeds, doc_dicts

class   UpperCAmelCase    (      a__ ):






   def __init__(						self :							List[Any]    ,       UpperCamelCase_ :							Optional[int]    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							Tuple    ,       UpperCamelCase_ :							Dict    ,       UpperCamelCase_ :							Dict=None  )->      Optional[Any]:
     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__    ,       )

     _lowercase:			Any      =     retrieval_workers
     _lowercase:			Optional[int]      =     question_encoder_tokenizer
     _lowercase:			Optional[Any]      =     generator_tokenizer
     if len(self.retrieval_workers  ) > 0:
       ray.get(
           [
               worker.create_rag_retriever.remote(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__  )
               for worker in self.retrieval_workers
           ]  )






   def 			_a    (						self :							int  )->      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 			_a    (						self :							str    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							Any  )->      Union[str, Any]:
     if len(self.retrieval_workers  ) > 0:
       # Select a random retrieval actor.
       _lowercase:			int      =     self.retrieval_workers[random.randint(0    ,       len(self.retrieval_workers  ) - 1  )]
       _lowercase  ,       _lowercase  ,       _lowercase:			List[Any]      =     ray.get(
           random_worker.retrieve.remote(lowercase__    ,       lowercase__  )  )
     else:
       _lowercase  ,       _lowercase:			str      =     self._main_retrieve(lowercase__    ,       lowercase__  )
       _lowercase:			Union[str, Any]      =     self.index.get_doc_dicts(lowercase__  )
     return retrieved_doc_embeds, doc_ids, doc_dicts






   @classmethod
   def 			_a    (						cls :							Dict    ,       UpperCamelCase_ :							Optional[Any]    ,       UpperCamelCase_ :							List[str]=None    ,       **UpperCamelCase_ :							int  )->      int:
     return super(lowercase__    ,       cls  ).get_tokenizers(lowercase__    ,       lowercase__    ,       **lowercase__  )






   @classmethod
   def 			_a    (						cls :							Tuple    ,       UpperCamelCase_ :							Any    ,       UpperCamelCase_ :							str    ,       UpperCamelCase_ :							List[Any]=None    ,       **UpperCamelCase_ :							Union[str, Any]  )->      Optional[int]:
     _lowercase:			Optional[int]      =     kwargs.pop('config'    ,       lowercase__  ) or RagConfig.from_pretrained(lowercase__    ,       **lowercase__  )
     _lowercase:			Union[str, Any]      =     RagTokenizer.from_pretrained(lowercase__    ,       config=lowercase__  )
     _lowercase:			Any      =     rag_tokenizer.question_encoder
     _lowercase:			List[str]      =     rag_tokenizer.generator

     if indexed_dataset is not None:
       _lowercase:			int      =     'custom'
       _lowercase:			str      =     CustomHFIndex(config.retrieval_vector_size    ,       lowercase__  )
     else:
       _lowercase:			Tuple      =     cls._build_index(lowercase__  )

     return cls(
         lowercase__    ,       question_encoder_tokenizer=lowercase__    ,       generator_tokenizer=lowercase__    ,       retrieval_workers=lowercase__    ,       index=lowercase__    ,       )






   def 			_a    (						self :							Optional[Any]  )->      str:
     logger.info('re-loading the new dataset with embeddings'  )
     # access from the training loop

     ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]  )

     # build the index object again
     _lowercase:			Tuple      =     self._build_index(self.config  )

     ray.get(
         [
             worker.create_rag_retriever.remote(
                 self.config    ,       self.question_encoder_tokenizer    ,       self.generator_tokenizer    ,       lowercase__  )
             for worker in self.retrieval_workers
         ]  )






from __future__ import annotations

from typing import Any






def 				UpperCAmelCase__  (							*_A			):






  """simple docstring"""


  if all(_check_not_integer(_A			) for m in matrix_s			):
    for i in matrix_s[1:]:
      _verify_matrix_sizes(matrix_s[0]      ,      _A			)
    return [[sum(_A			) for t in zip(*_A			)] for m in zip(*_A			)]
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if (
      _check_not_integer(_A			)
      and _check_not_integer(_A			)
      and _verify_matrix_sizes(_A      ,      _A			)
  ):
    return [[i - j for i, j in zip(*_A			)] for m in zip(_A      ,      _A			)]
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  return [[x * n for x in row] for row in matrix]






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if _check_not_integer(_A			) and _check_not_integer(_A			):
    a_    , a_		    =  _verify_matrix_sizes(_A      ,      _A			)

  if cols[0] != rows[1]:
    a_		    =  (
        '''Cannot multiply matrix of dimensions '''
        f"({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"
    )
    raise ValueError(_A			)
  return [
      [sum(m * n for m, n in zip(_A      ,      _A			)			) for j in zip(*_A			)] for i in matrix_a
  ]






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  int(_A			)
  return [[int(row == column			) for column in range(_A			)] for row in range(_A			)]






def 				UpperCAmelCase__  (							_A      ,      _A = True			):






  """simple docstring"""


  if _check_not_integer(_A			):
    if return_map:
      return map(_A      ,      zip(*_A			)			)
    else:
      return list(map(_A      ,      zip(*_A			)			)			)
  raise TypeError('''Expected a matrix, got int/list instead'''			)






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  matrix[:row] + matrix[row + 1 :]
  return [row[:column] + row[column + 1 :] for row in minor]






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  if len(_A			) == 1:
    return matrix[0][0]

  return sum(
      x * determinant(minor(_A      ,      0      ,      _A			)			) * (-1) ** i
      for i, x in enumerate(matrix[0]			)			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  determinant(_A			)
  if det == 0:
    return None

  a_		    =  [
      [determinant(minor(_A      ,      _A      ,      _A			)			) for j in range(len(_A			)			)]
      for i in range(len(_A			)			)
  ]

  a_		    =  [
      [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]			)]
      for row in range(len(_A			)			)
  ]
  a_		    =  list(transpose(_A			)			)
  return scalar_multiply(_A      ,      1 / det			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return not isinstance(_A      ,      _A			) and not isinstance(matrix[0]      ,      _A			)






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  return len(_A			), len(matrix[0]			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  _shape(_A			) + _shape(_A			)
  if shape[0] != shape[3] or shape[1] != shape[2]:
    a_		    =  (
        '''operands could not be broadcast together with shape '''
        f"({shape[0], shape[1]}), ({shape[2], shape[3]})"
    )
    raise ValueError(_A			)
  return (shape[0], shape[2]), (shape[1], shape[3])






def 				UpperCAmelCase__  (							):






  """simple docstring"""


  a_		    =  [[12, 10], [3, 9]]
  a_		    =  [[3, 4], [7, 4]]
  a_		    =  [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
  a_		    =  [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
  print(f"Add Operation, {add(_A      ,      _A			) = } \n"			)
  print(
      f"Multiply Operation, {multiply(_A      ,      _A			) = } \n"      ,      )
  print(f"Identity: {identity(5			)}\n"			)
  print(f"Minor of {matrix_c} = {minor(_A      ,      1      ,      2			)} \n"			)
  print(f"Determinant of {matrix_b} = {determinant(_A			)} \n"			)
  print(f"Inverse of {matrix_d} = {inverse(_A			)}\n"			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()
