






def       snake_case  (A_				:float     ,      A_				:float     ,      A_				:int				):







  '''simple docstring'''

  if principal <= 0:
    raise Exception('Principal borrowed must be > 0'				)
  if rate_per_annum < 0:
    raise Exception('Rate of interest must be >= 0'				)
  if years_to_repay <= 0 or not isinstance(A_     ,      A_				):
    raise Exception('Years to repay must be an integer > 0'				)

  # Yearly rate is divided by 12 to get monthly rate
  a					:      Optional[int]    =       rate_per_annum / 1_2

  # Years to repay is multiplied by 12 to get number of payments as payment is monthly
  a					:      Optional[Any]    =       years_to_repay * 1_2

  return (
      principal
      * rate_per_month
      * (1 + rate_per_month) ** number_of_payments
      / ((1 + rate_per_month) ** number_of_payments - 1)
  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()












"""simple docstring"""


from typing import TYPE_CHECKING

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


_UpperCamelCase    :   Optional[int]   			=						{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Optional[int]   			=						[
       '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

   _UpperCamelCase    :   int   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




