"""This module contains the main wrapper class."""


class BaseWrapper:
    """Define base template for function wrapper classes. """

    def __init__(self, func):
        self.func = func
        self.__doc__ = func.__doc__

    def __call__(self, *args, **kwargs):
        raise NotImplementedError


class NumpyWrapper(BaseWrapper):
    """Function wrapper for numpy's random functions. Allows easy usage
    avoiding the creation anonymous lambda functions. In addition, the `size`
    attribute is adjusted automatically. 
    
    For instance, instead of writing 
    'lambda x: np.random.randint(low=1, high=10, size=x.shape[0])' 
    
    you may simply write 
    'ts.random.randint(low=1, high=10)'.
    
    """


    def __init__(self, func, size="arg"):
        super(NumpyWrapper, self).__init__(func)
        self.size = size

    def __call__(self, *args, **kwargs):

        if self.size == "arg":
            def wrapped(x):
                return self.func(*args, x.shape[0], **kwargs)

        elif self.size == "kwarg":
            def wrapped(x):
                return self.func(*args, size=x.shape[0], **kwargs)

        else:
            raise ValueError("Size argument must be 'arg' or 'kwarg'.")

        wrapped.__doc__ = self.func.__doc__
        return wrapped

"""This module contains the main wrapper class."""


class BaseWrapper:
    """Define base template for function wrapper classes. """

    def __init__(self, func):
        self.func = func
        self.__doc__ = func.__doc__

    def __call__(self, *args, **kwargs):
        raise NotImplementedError


class NumpyWrapper(BaseWrapper):
    """Function wrapper for numpy's random functions. Allows easy usage
    avoiding the creation anonymous lambda functions. In addition, the `size`
    attribute is adjusted automatically. 
    
    For instance, instead of writing 
    'lambda x: np.random.randint(low=1, high=10, size=x.shape[0])' 
    
    you may simply write 
    'ts.random.randint(low=1, high=10)'.
    
    """


    def __init__(self, func, size="arg"):
        super(NumpyWrapper, self).__init__(func)
        self.size = size

    def __call__(self, *args, **kwargs):

        if self.size == "arg":
            def wrapped(x):
                return self.func(x.shape[0], *args, **kwargs)

        elif self.size == "kwarg":
            def wrapped(x):
                return self.func(*args, size=x.shape[0], **kwargs)

        else:
            raise ValueError("Size argument must be 'arg' or 'kwarg'.")

        wrapped.__doc__ = self.func.__doc__
        return wrapped

Fix bug due to wrong arguments order.
Fix bug due to wrong arguments order.

  """This module contains the main wrapper class."""
  
  
  class BaseWrapper:
      """Define base template for function wrapper classes. """
  
      def __init__(self, func):
          self.func = func
          self.__doc__ = func.__doc__
  
      def __call__(self, *args, **kwargs):
          raise NotImplementedError
  
  
  class NumpyWrapper(BaseWrapper):
      """Function wrapper for numpy's random functions. Allows easy usage
      avoiding the creation anonymous lambda functions. In addition, the `size`
      attribute is adjusted automatically. 
      
      For instance, instead of writing 
      'lambda x: np.random.randint(low=1, high=10, size=x.shape[0])' 
      
      you may simply write 
      'ts.random.randint(low=1, high=10)'.
      
      """
  
  
      def __init__(self, func, size="arg"):
          super(NumpyWrapper, self).__init__(func)
          self.size = size
  
      def __call__(self, *args, **kwargs):
  
          if self.size == "arg":
              def wrapped(x):
-                 return self.func(*args, x.shape[0], **kwargs)
+                 return self.func(x.shape[0], *args, **kwargs)
  
          elif self.size == "kwarg":
              def wrapped(x):
                  return self.func(*args, size=x.shape[0], **kwargs)
  
          else:
              raise ValueError("Size argument must be 'arg' or 'kwarg'.")
  
          wrapped.__doc__ = self.func.__doc__
          return wrapped
  
Fix bug due to wrong arguments order.
## Code Before:
"""This module contains the main wrapper class."""


class BaseWrapper:
    """Define base template for function wrapper classes. """

    def __init__(self, func):
        self.func = func
        self.__doc__ = func.__doc__

    def __call__(self, *args, **kwargs):
        raise NotImplementedError


class NumpyWrapper(BaseWrapper):
    """Function wrapper for numpy's random functions. Allows easy usage
    avoiding the creation anonymous lambda functions. In addition, the `size`
    attribute is adjusted automatically. 
    
    For instance, instead of writing 
    'lambda x: np.random.randint(low=1, high=10, size=x.shape[0])' 
    
    you may simply write 
    'ts.random.randint(low=1, high=10)'.
    
    """


    def __init__(self, func, size="arg"):
        super(NumpyWrapper, self).__init__(func)
        self.size = size

    def __call__(self, *args, **kwargs):

        if self.size == "arg":
            def wrapped(x):
                return self.func(*args, x.shape[0], **kwargs)

        elif self.size == "kwarg":
            def wrapped(x):
                return self.func(*args, size=x.shape[0], **kwargs)

        else:
            raise ValueError("Size argument must be 'arg' or 'kwarg'.")

        wrapped.__doc__ = self.func.__doc__
        return wrapped

## Instruction:
Fix bug due to wrong arguments order.
## Code After:
"""This module contains the main wrapper class."""


class BaseWrapper:
    """Define base template for function wrapper classes. """

    def __init__(self, func):
        self.func = func
        self.__doc__ = func.__doc__

    def __call__(self, *args, **kwargs):
        raise NotImplementedError


class NumpyWrapper(BaseWrapper):
    """Function wrapper for numpy's random functions. Allows easy usage
    avoiding the creation anonymous lambda functions. In addition, the `size`
    attribute is adjusted automatically. 
    
    For instance, instead of writing 
    'lambda x: np.random.randint(low=1, high=10, size=x.shape[0])' 
    
    you may simply write 
    'ts.random.randint(low=1, high=10)'.
    
    """


    def __init__(self, func, size="arg"):
        super(NumpyWrapper, self).__init__(func)
        self.size = size

    def __call__(self, *args, **kwargs):

        if self.size == "arg":
            def wrapped(x):
                return self.func(x.shape[0], *args, **kwargs)

        elif self.size == "kwarg":
            def wrapped(x):
                return self.func(*args, size=x.shape[0], **kwargs)

        else:
            raise ValueError("Size argument must be 'arg' or 'kwarg'.")

        wrapped.__doc__ = self.func.__doc__
        return wrapped
