


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE				:						List[str]			   = {
    "configuration_realm": ["REALM_PRETRAINED_CONFIG_ARCHIVE_MAP", "RealmConfig"],
    "tokenization_realm": ["RealmTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE				:						Tuple			   = ["RealmTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE				:						List[str]			   = [
       "REALM_PRETRAINED_MODEL_ARCHIVE_LIST",
       "RealmEmbedder",
       "RealmForOpenQA",
       "RealmKnowledgeAugEncoder",
       "RealmPreTrainedModel",
       "RealmReader",
       "RealmScorer",
       "load_tf_weights_in_realm",
   ]
   SCREAMING_SNAKE_CASE				:						Optional[int]			   = ["RealmRetriever"]


if TYPE_CHECKING:
   from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
   from .tokenization_realm import RealmTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_realm import RealmTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_realm import (
          REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RealmEmbedder,
          RealmForOpenQA,
          RealmKnowledgeAugEncoder,
          RealmPreTrainedModel,
          RealmReader,
          RealmScorer,
          load_tf_weights_in_realm,
      )
      from .retrieval_realm import RealmRetriever


else:
   import sys

   SCREAMING_SNAKE_CASE				:						Tuple			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



"""simple docstring"""

import doctest

import projectq
from projectq.ops import H, Measure




def 			lowercase__  (    lowerCAmelCase :    projectq.cengines._main.MainEngine       )       ->					int:






    """simple docstring"""




    UpperCAmelCase  	=    quantum_engine.allocate_qubit()
    H | qubit
    Measure | qubit
    return int(lowerCAmelCase       )


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   SCREAMING_SNAKE_CASE_			    =  projectq.MainEngine()

   # Generate a list of 10 random numbers
   SCREAMING_SNAKE_CASE_			    =  [get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print('''Random numbers''', random_numbers_list)



