


"""simple docstring"""







import random


def      lowercase			(				A_  ,   A_       )->      tuple:
  '''simple docstring'''







  a							,		a							,		a						:		List[str]      							=			[], [], []
  for element in data:
    if element < pivot:
      less.append(A_       )
    elif element > pivot:
      greater.append(A_       )
    else:
      equal.append(A_       )
  return less, equal, greater







def      lowercase			(				A_  ,   A_       )->      Dict:
  '''simple docstring'''







  if index >= len(A_       ) or index < 0:
    return None

  a						:		Optional[Any]      							=			items[random.randint(0  ,   len(A_       ) - 1       )]
  a						:		int      							=			0
  a							,		a							,		a						:		Any      							=			_partition(A_  ,   A_       )
  a						:		Union[str, Any]      							=			len(A_       )
  a						:		Any      							=			len(A_       )

  # index is the pivot
  if m <= index < m + count:
    return pivot
  # must be in smaller
  elif m > index:
    return quick_select(A_  ,   A_       )
  # must be in larger
  else:
    return quick_select(A_  ,   index - (m + count)       )










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowercase    					=      {
    """configuration_rag""": ["""RagConfig"""],
    """retrieval_rag""": ["""RagRetriever"""],
    """tokenization_rag""": ["""RagTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """RagModel""",
     """RagPreTrainedModel""",
     """RagSequenceForGeneration""",
     """RagTokenForGeneration""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    					=      [
     """TFRagModel""",
     """TFRagPreTrainedModel""",
     """TFRagSequenceForGeneration""",
     """TFRagTokenForGeneration""",
 ]


if TYPE_CHECKING:
 from .configuration_rag import RagConfig
 from .retrieval_rag import RagRetriever
 from .tokenization_rag import RagTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rag import (
      TFRagModel,
      TFRagPreTrainedModel,
      TFRagSequenceForGeneration,
      TFRagTokenForGeneration,
  )

else:
 import sys

 __lowercase    					=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






