from __future__ import absolute_import, division, print_function

import re
import warnings
import weakref

import torch
from torch.distributions import constraints, transform_to


class ParamStoreDict(object):
    """
    Global store for parameters in Pyro. This is basically a key-value store.
    The typical user interacts with the ParamStore primarily through the
    primitive `pyro.param`.

    See `Intro Part II <http://pyro.ai/examples/intro_part_ii.html>`_ for further discussion
    and `SVI Part I <http://pyro.ai/examples/svi_part_i.html>`_ for some examples.

    Some things to bear in mind when using parameters in Pyro:

    - parameters must be assigned unique names
    - the `init_tensor` argument to `pyro.param` is only used the first time that a given (named)
      parameter is registered with Pyro.
    - for this reason, a user may need to use the `clear()` method if working in a REPL in order to
      get the desired behavior. this method can also be invoked with `pyro.clear_param_store()`.
    - the internal name of a parameter within a PyTorch `nn.Module` that has been registered with
      Pyro is prepended with the Pyro name of the module. so nothing prevents the user from having
      two different modules each of which contains a parameter named `weight`. by contrast, a user
      can only have one top-level parameter named `weight` (outside of any module).
    - parameters can be saved and loaded from disk using `save` and `load`.
    """

    # -------------------------------------------------------------------------------
    # New dict-like interface

    def __init__(self):
        """
        initialize ParamStore data structures
        """
        self._params = {}  # dictionary from param name to param
        self._param_to_name = {}  # dictionary from unconstrained param to param name
        self._constraints = {}  # dictionary from param name to constraint object

    def clear(self):
        """
        Clear the ParamStore
        """
        self._params = {}
        self._param_to_name = {}
        self._constraints = {}

    def items(self):
        """
        Iterate over ``(name, constrained_param)`` pairs.
        """
        for name in self._params:
            yield name, self[name]

    def keys(self):
        """
        Iterate over param names.
        """
        return self._params.keys()

    def values(self):
        """
        Iterate over constrained parameter values.
        """
        for name, constrained_param in self.items():
            yield constrained_param

    def __bool__(self):
        return bool(self._params)

    def __len__(self):
        return len(self._params)

    def __contains__(self, name):
        return name in self._params

    def __iter__(self):
        """
        Iterate over param names.
        """
        return iter(self.keys())

    def __delitem__(self, name):
        """
        Remove a parameter from the param store.
        """
        constrained_value = self._params.pop(name)
        unconstrained_value = constrained_value.unconstrained()
        self._param_to_name.pop(unconstrained_value)
        self._constraints.pop(name)

    def __getitem__(self, name):
        """
        Get the constrained value of a named parameter.
        """
        unconstrained_value = self._params[name]

        # compute the constrained value
        constraint = self._constraints[name]
        constrained_value = transform_to(constraint)(unconstrained_value)
        constrained_value.unconstrained = weakref.ref(unconstrained_value)

        return constrained_value

    def __setitem__(self, name, new_constrained_value):
        """
        Set the constrained value of an existing parameter, or the value of a
        new unconstrained parameter. To declare a new parameter with
        constraint, use :meth:`setdefault`.
        """
        # store constraint, defaulting to unconstrained
        constraint = self._constraints.setdefault(name, constraints.real)

        # compute the unconstrained value
        with torch.no_grad():
            # FIXME should we .detach() the new_constrained_value?
            unconstrained_value = transform_to(constraint).inv(new_constrained_value)
            unconstrained_value = unconstrained_value.contiguous()
        unconstrained_value.requires_grad_(True)

        # store a bidirectional mapping between name and unconstrained tensor
        self._params[name] = unconstrained_value
        self._param_to_name[unconstrained_value] = name

    def setdefault(self, name, init_constrained_value, constraint=constraints.real):
        """
        Retrieve a constrained parameter value from the if it exists, otherwise
        set the initial value. Note that this is a little fancier than
        :meth:`dict.setdefault`.

        If the parameter already exists, ``init_constrained_tensor`` will be ignored. To avoid
        expensive creation of ``init_constrained_tensor`` you can wrap it in a ``lambda`` that
        will only be evaluated if the parameter does not already exist::

            param_store.get("foo", lambda: (0.001 * torch.randn(1000, 1000)).exp(),
                            constraint=constraints.positive)

        :param str name: parameter name
        :param init_constrained_value: initial constrained value
        :type init_constrained_value: torch.Tensor or callable returning a torch.Tensor
        :param constraint: torch constraint object
        :type constraint: torch.distributions.constraints.Constraint
        :returns: constrained parameter value
        :rtype: torch.Tensor
        """
        if name not in self._params:
            # set the constraint
            self._constraints[name] = constraint

            # evaluate the lazy value
            if callable(init_constrained_value):
                init_constrained_value = init_constrained_value()

            # set the initial value
            self[name] = init_constrained_value

        # get the param, which is guaranteed to exist
        return self[name]

    # -------------------------------------------------------------------------------
    # Old non-dict interface

    def named_parameters(self):
        """
        Returns an iterator over ``(name, unconstrained_value)`` tuples for
        each parameter in the ParamStore.
        """
        return self._params.items()

    def get_all_param_names(self):
        warnings.warn("ParamStore.get_all_param_names() is deprecated; use .keys() instead.",
                      DeprecationWarning)
        return self.keys()

    def replace_param(self, param_name, new_param, old_param):
        warnings.warn("ParamStore.replace_param() is deprecated; use .__setitem__() instead.",
                      DeprecationWarning)
        assert self._params[param_name] is old_param.unconstrained()
        self[param_name] = new_param

    def get_param(self, name, init_tensor=None, constraint=constraints.real, event_dim=None):
        """
        Get parameter from its name. If it does not yet exist in the
        ParamStore, it will be created and stored.
        The Pyro primitive `pyro.param` dispatches to this method.

        :param name: parameter name
        :type name: str
        :param init_tensor: initial tensor
        :type init_tensor: torch.Tensor
        :param constraint: torch constraint
        :type constraint: torch.distributions.constraints.Constraint
        :param int event_dim: (ignored)
        :returns: parameter
        :rtype: torch.Tensor
        """
        if init_tensor is None:
            return self[name]
        else:
            return self.setdefault(name, init_tensor, constraint)

    def match(self, name):
        """
        Get all parameters that match regex. The parameter must exist.

        :param name: regular expression
        :type name: str
        :returns: dict with key param name and value torch Tensor
        """
        pattern = re.compile(name)
        return {name: self[name] for name in self if pattern.match(name)}

    def param_name(self, p):
        """
        Get parameter name from parameter

        :param p: parameter
        :returns: parameter name
        """
        return self._param_to_name.get(p)

    def get_state(self):
        """
        Get the ParamStore state.
        """
        state = {
            'params': self._params,
            'constraints': self._constraints,
        }
        return state

    def set_state(self, state):
        """
        Set the ParamStore state using state from a previous get_state() call
        """
        assert isinstance(state, dict), "malformed ParamStore state"
        assert set(state.keys()) == set(['params', 'constraints']), \
            "malformed ParamStore keys {}".format(state.keys())

        for param_name, param in state['params'].items():
            self._params[param_name] = param
            self._param_to_name[param] = param_name

        for param_name, constraint in state['constraints'].items():
            if isinstance(constraint, type(constraints.real)):
                # Work around lack of hash & equality comparison on constraints.
                constraint = constraints.real
            self._constraints[param_name] = constraint

    def save(self, filename):
        """
        Save parameters to disk

        :param filename: file name to save to
        :type filename: str
        """
        with open(filename, "wb") as output_file:
            torch.save(self.get_state(), output_file)

    def load(self, filename, map_location=None):
        """
        Loads parameters from disk

        .. note::

           If using :meth:`pyro.module` on parameters loaded from
           disk, be sure to set the ``update_module_params`` flag::

               pyro.get_param_store().load('saved_params.save')
               pyro.module('module', nn, update_module_params=True)

        :param filename: file name to load from
        :type filename: str
        :param map_location: specifies how to remap storage locations
        :type map_location: function, torch.device, string or a dict
        """
        with open(filename, "rb") as input_file:
            state = torch.load(input_file, map_location)
        self.set_state(state)


# used to create fully-formed param names, e.g. mymodule$$$mysubmodule.weight
_MODULE_NAMESPACE_DIVIDER = "$$$"


def param_with_module_name(pyro_name, param_name):
    return _MODULE_NAMESPACE_DIVIDER.join([pyro_name, param_name])


def module_from_param_with_module_name(param_name):
    return param_name.split(_MODULE_NAMESPACE_DIVIDER)[0]


def user_param_name(param_name):
    if _MODULE_NAMESPACE_DIVIDER in param_name:
        return param_name.split(_MODULE_NAMESPACE_DIVIDER)[1]
    return param_name

pyro/params/param_store.py
from __future__ import absolute_import, division, print_function

import re
import warnings
import weakref

import torch
from torch.distributions import constraints, transform_to


class ParamStoreDict(object):
    """
    Global store for parameters in Pyro. This is basically a key-value store.
    The typical user interacts with the ParamStore primarily through the
    primitive `pyro.param`.

    See `Intro Part II <http://pyro.ai/examples/intro_part_ii.html>`_ for further discussion
    and `SVI Part I <http://pyro.ai/examples/svi_part_i.html>`_ for some examples.

    Some things to bear in mind when using parameters in Pyro:

    - parameters must be assigned unique names
    - the `init_tensor` argument to `pyro.param` is only used the first time that a given (named)
      parameter is registered with Pyro.
    - for this reason, a user may need to use the `clear()` method if working in a REPL in order to
      get the desired behavior. this method can also be invoked with `pyro.clear_param_store()`.
    - the internal name of a parameter within a PyTorch `nn.Module` that has been registered with
      Pyro is prepended with the Pyro name of the module. so nothing prevents the user from having
      two different modules each of which contains a parameter named `weight`. by contrast, a user
      can only have one top-level parameter named `weight` (outside of any module).
    - parameters can be saved and loaded from disk using `save` and `load`.
    """

    # -------------------------------------------------------------------------------
    # New dict-like interface

    def __init__(self):
        """
        initialize ParamStore data structures
        """
        self._params = {}  # dictionary from param name to param
        self._param_to_name = {}  # dictionary from unconstrained param to param name
        self._constraints = {}  # dictionary from param name to constraint object

    def clear(self):
        """
        Clear the ParamStore
        """
        self._params = {}
        self._param_to_name = {}
        self._constraints = {}

    def items(self):
        """
        Iterate over ``(name, constrained_param)`` pairs.
        """
        for name in self._params:
            yield name, self[name]

    def keys(self):
        """
        Iterate over param names.
        """
        return self._params.keys()

    def values(self):
        """
        Iterate over constrained parameter values.
        """
        for name, constrained_param in self.items():
            yield constrained_param

    def __bool__(self):
        return bool(self._params)

    def __len__(self):
        return len(self._params)

    def __contains__(self, name):
        return name in self._params

    def __iter__(self):
        """
        Iterate over param names.
        """
        return iter(self.keys())

    def __delitem__(self, name):
        """
        Remove a parameter from the param store.
        """
        unconstrained_value = self._params.pop(name)
        self._param_to_name.pop(unconstrained_value)
        self._constraints.pop(name)

    def __getitem__(self, name):
        """
        Get the constrained value of a named parameter.
        """
        unconstrained_value = self._params[name]

        # compute the constrained value
        constraint = self._constraints[name]
        constrained_value = transform_to(constraint)(unconstrained_value)
        constrained_value.unconstrained = weakref.ref(unconstrained_value)

        return constrained_value

    def __setitem__(self, name, new_constrained_value):
        """
        Set the constrained value of an existing parameter, or the value of a
        new unconstrained parameter. To declare a new parameter with
        constraint, use :meth:`setdefault`.
        """
        # store constraint, defaulting to unconstrained
        constraint = self._constraints.setdefault(name, constraints.real)

        # compute the unconstrained value
        with torch.no_grad():
            # FIXME should we .detach() the new_constrained_value?
            unconstrained_value = transform_to(constraint).inv(new_constrained_value)
            unconstrained_value = unconstrained_value.contiguous()
        unconstrained_value.requires_grad_(True)

        # store a bidirectional mapping between name and unconstrained tensor
        self._params[name] = unconstrained_value
        self._param_to_name[unconstrained_value] = name

    def setdefault(self, name, init_constrained_value, constraint=constraints.real):
        """
        Retrieve a constrained parameter value from the if it exists, otherwise
        set the initial value. Note that this is a little fancier than
        :meth:`dict.setdefault`.

        If the parameter already exists, ``init_constrained_tensor`` will be ignored. To avoid
        expensive creation of ``init_constrained_tensor`` you can wrap it in a ``lambda`` that
        will only be evaluated if the parameter does not already exist::

            param_store.get("foo", lambda: (0.001 * torch.randn(1000, 1000)).exp(),
                            constraint=constraints.positive)

        :param str name: parameter name
        :param init_constrained_value: initial constrained value
        :type init_constrained_value: torch.Tensor or callable returning a torch.Tensor
        :param constraint: torch constraint object
        :type constraint: torch.distributions.constraints.Constraint
        :returns: constrained parameter value
        :rtype: torch.Tensor
        """
        if name not in self._params:
            # set the constraint
            self._constraints[name] = constraint

            # evaluate the lazy value
            if callable(init_constrained_value):
                init_constrained_value = init_constrained_value()

            # set the initial value
            self[name] = init_constrained_value

        # get the param, which is guaranteed to exist
        return self[name]

    # -------------------------------------------------------------------------------
    # Old non-dict interface

    def named_parameters(self):
        """
        Returns an iterator over ``(name, unconstrained_value)`` tuples for
        each parameter in the ParamStore.
        """
        return self._params.items()

    def get_all_param_names(self):
        warnings.warn("ParamStore.get_all_param_names() is deprecated; use .keys() instead.",
                      DeprecationWarning)
        return self.keys()

    def replace_param(self, param_name, new_param, old_param):
        warnings.warn("ParamStore.replace_param() is deprecated; use .__setitem__() instead.",
                      DeprecationWarning)
        assert self._params[param_name] is old_param.unconstrained()
        self[param_name] = new_param

    def get_param(self, name, init_tensor=None, constraint=constraints.real, event_dim=None):
        """
        Get parameter from its name. If it does not yet exist in the
        ParamStore, it will be created and stored.
        The Pyro primitive `pyro.param` dispatches to this method.

        :param name: parameter name
        :type name: str
        :param init_tensor: initial tensor
        :type init_tensor: torch.Tensor
        :param constraint: torch constraint
        :type constraint: torch.distributions.constraints.Constraint
        :param int event_dim: (ignored)
        :returns: parameter
        :rtype: torch.Tensor
        """
        if init_tensor is None:
            return self[name]
        else:
            return self.setdefault(name, init_tensor, constraint)

    def match(self, name):
        """
        Get all parameters that match regex. The parameter must exist.

        :param name: regular expression
        :type name: str
        :returns: dict with key param name and value torch Tensor
        """
        pattern = re.compile(name)
        return {name: self[name] for name in self if pattern.match(name)}

    def param_name(self, p):
        """
        Get parameter name from parameter

        :param p: parameter
        :returns: parameter name
        """
        return self._param_to_name.get(p)

    def get_state(self):
        """
        Get the ParamStore state.
        """
        state = {
            'params': self._params,
            'constraints': self._constraints,
        }
        return state

    def set_state(self, state):
        """
        Set the ParamStore state using state from a previous get_state() call
        """
        assert isinstance(state, dict), "malformed ParamStore state"
        assert set(state.keys()) == set(['params', 'constraints']), \
            "malformed ParamStore keys {}".format(state.keys())

        for param_name, param in state['params'].items():
            self._params[param_name] = param
            self._param_to_name[param] = param_name

        for param_name, constraint in state['constraints'].items():
            if isinstance(constraint, type(constraints.real)):
                # Work around lack of hash & equality comparison on constraints.
                constraint = constraints.real
            self._constraints[param_name] = constraint

    def save(self, filename):
        """
        Save parameters to disk

        :param filename: file name to save to
        :type filename: str
        """
        with open(filename, "wb") as output_file:
            torch.save(self.get_state(), output_file)

    def load(self, filename, map_location=None):
        """
        Loads parameters from disk

        .. note::

           If using :meth:`pyro.module` on parameters loaded from
           disk, be sure to set the ``update_module_params`` flag::

               pyro.get_param_store().load('saved_params.save')
               pyro.module('module', nn, update_module_params=True)

        :param filename: file name to load from
        :type filename: str
        :param map_location: specifies how to remap storage locations
        :type map_location: function, torch.device, string or a dict
        """
        with open(filename, "rb") as input_file:
            state = torch.load(input_file, map_location)
        self.set_state(state)


# used to create fully-formed param names, e.g. mymodule$$$mysubmodule.weight
_MODULE_NAMESPACE_DIVIDER = "$$$"


def param_with_module_name(pyro_name, param_name):
    return _MODULE_NAMESPACE_DIVIDER.join([pyro_name, param_name])


def module_from_param_with_module_name(param_name):
    return param_name.split(_MODULE_NAMESPACE_DIVIDER)[0]


def user_param_name(param_name):
    if _MODULE_NAMESPACE_DIVIDER in param_name:
        return param_name.split(_MODULE_NAMESPACE_DIVIDER)[1]
    return param_name

pyro/params/param_store.py
diff --git a/pyro/params/param_store.py b/pyro/params/param_store.py
index 7dd44043a1..2ca60fd12b 100644
--- a/pyro/params/param_store.py
+++ b/pyro/params/param_store.py
@@ -89,8 +89,7 @@ def __delitem__(self, name):
         """
         Remove a parameter from the param store.
         """
-        constrained_value = self._params.pop(name)
-        unconstrained_value = constrained_value.unconstrained()
+        unconstrained_value = self._params.pop(name)
         self._param_to_name.pop(unconstrained_value)
         self._constraints.pop(name)
 
diff --git a/tests/params/test_param.py b/tests/params/test_param.py
index 459e3b2fa7..416177daf0 100644
--- a/tests/params/test_param.py
+++ b/tests/params/test_param.py
@@ -123,3 +123,13 @@ def test_dict_interface():
     assert param_store['y'].shape == (4, 5)
     assert_equal(param_store.setdefault('y', torch.zeros(4, 5)), torch.ones(4, 5))
     assert_equal(param_store['y'].unconstrained(), torch.zeros(4, 5))
+
+    # remove y
+    del param_store['y']
+    assert not param_store
+    assert len(param_store) == 0
+    assert 'x' not in param_store
+    assert 'y' not in param_store
+    assert list(param_store.keys()) == []
+    assert list(key for key, value in param_store.items()) == []
+    assert len(list(param_store.values())) == 0

Cannot delete constrained parameter [bug]
### **Issue Description**
Deleting a constrained parameter throws an error.
In the function `param_store.__delitem__()`, the line 
`unconstrained_value = constrained_value.unconstrained()`
throws
`AttributeError: 'Tensor' object has no attribute 'unconstrained'`

### **Environment**
OS: Windows 8.1
Python Version: 3.6.8
Pytorch Version: 1.1.0
Pyro Version: 0.3.3

This error was also present using Pytorch 1.0 and Pyro 0.3.1.
### **Code Snippet**
```py
import torch
import pyro
from torch.distributions import constraints

param_store = pyro.get_param_store()
a = pyro.param('a', torch.ones(3))
print(param_store.keys())  #dict_keys(['a'])
param_store.__delitem__('a')  #Works fine
print(param_store.keys())  #dict_keys([])
b = pyro.param('b', torch.ones(3), constraint=constraints.positive)
print(param_store.keys())  #dict_keys(['b'])
param_store.__delitem__('b')  #AttributeError: 'Tensor' object has no attribute 'unconstrained'
```
