
"""simple docstring"""
import numpy as np
def 					a__ ( a :					List[Any]							,  a :					List[Any]		):

  """simple docstring"""







  _snake_case  :    List[str]            = np.array(a		)
  _snake_case  :    Optional[int]            = np.array(a		)

  _snake_case  :    Union[str, Any]            = abs(predict - actual		)
  _snake_case  :    Any            = difference.mean()

  return score
def 					a__ ( a :					Any							,  a :					str		):

  """simple docstring"""







  _snake_case  :    List[str]            = np.array(a		)
  _snake_case  :    Tuple            = np.array(a		)

  _snake_case  :    str            = predict - actual
  _snake_case  :    Optional[int]            = np.square(a		)

  _snake_case  :    Dict            = square_diff.mean()
  return score
def 					a__ ( a :					Tuple							,  a :					int		):

  """simple docstring"""







  _snake_case  :    str            = np.array(a		)
  _snake_case  :    Optional[Any]            = np.array(a		)

  _snake_case  :    List[str]            = predict - actual
  _snake_case  :    Optional[int]            = np.square(a		)
  _snake_case  :    Union[str, Any]            = square_diff.mean()
  _snake_case  :    Optional[int]            = np.sqrt(a		)
  return score
def 					a__ ( a :					int							,  a :					Union[str, Any]		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = np.array(a		)
  _snake_case  :    Union[str, Any]            = np.array(a		)

  _snake_case  :    Union[str, Any]            = np.log(predict + 1		)
  _snake_case  :    int            = np.log(actual + 1		)

  _snake_case  :    Tuple            = log_predict - log_actual
  _snake_case  :    Tuple            = np.square(a		)
  _snake_case  :    Optional[int]            = square_diff.mean()

  _snake_case  :    Dict            = np.sqrt(a		)

  return score
def 					a__ ( a :					str							,  a :					Dict		):

  """simple docstring"""







  _snake_case  :    List[str]            = np.array(a		)
  _snake_case  :    Union[str, Any]            = np.array(a		)

  _snake_case  :    int            = predict - actual
  _snake_case  :    str            = np.sum(a		) / len(a		)
  _snake_case  :    Any            = np.sum(a		) / len(a		)
  # print(numerator, denumerator)
  _snake_case  :    Tuple            = float(a		) / denumerator * 100

  return score



def 					a__ ( a :					str							,  a :					List[str]		):

  """simple docstring"""







  return np.mean(np.array(a		) == np.array(a		)		)





__SCREAMING_SNAKE_CASE	:							Optional[Any]           =  '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__SCREAMING_SNAKE_CASE	:							Optional[int]           =  [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__SCREAMING_SNAKE_CASE	:							Optional[int]           =  {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}

