# mypy: allow-untyped-decorators
# mypy: allow-untyped-defs
from functools import reduce
import torch
import operator
from torch.fx.tensor_type import Dyn, is_consistent, TensorType, is_more_precise
from typing import Callable, Dict
from torch.fx.node import Target, Node
from torch.nn.modules.batchnorm import BatchNorm2d
from torch.nn.modules.conv import Conv2d
from torch.fx.experimental.refinement_types import Equality
import itertools

from torch.fx.experimental.unification import Var  # type: ignore[attr-defined]

import sympy

_INFERENCE_RULES: Dict[Target, Callable] = {}
_REFINEMENT_RULES: Dict[Target, Callable] = {}
_RULES: Dict[Target, Callable] = {}


def expand_to_tensor_dim(t, n):
    """
    Expand a type to the desired tensor dimension if possible
    Raise an error otherwise.
    - t is the given type
    - n is a number of dimensions to expand to
    """
    if t == Dyn:
        dims = [Dyn] * n
        return TensorType(tuple(dims))
    elif isinstance(t, TensorType):
        if len(t.__args__) != n:
            raise TypeError(f'Cannot extend tensor. Tensor {t} has rank {len(t.__args__)}. It should have rank {n}')
        return t
    else:
        raise TypeError(f'Cannot match the type {t}')


def broadcast_types(t1, t2):
    """
    Applies broadcasting to both given types such that they
    become consistent with eachother and returns two new
    resulting types
    """

    # if either type is Dyn, do nothing since the types are already consistent
    if t1 == Dyn or t2 == Dyn or isinstance(t1, Var) or isinstance(t2, Var):
        return t1, t2

    if isinstance(t1, TensorType) and isinstance(t2, TensorType):
        s1 = len(t1.__args__)
        s2 = len(t2.__args__)

        new_t1 = list(t1.__args__)
        new_t2 = list(t2.__args__)

        # We make the types the same length which is the first requirement
        # for consistency
        if s1 > s2:
            for i in range(s1 - s2):
                new_t2.insert(0, 1)

        elif s2 > s1:
            for i in range(s2 - s1):
                new_t1.insert(0, 1)

        # we replace occurrences of "1" with each tensor with
        # the corresponding type from the other tensor
        for i, (x, y) in enumerate(zip(new_t1, new_t2)):
            if x == 1:
                new_t1[i] = y
            elif y == 1:
                new_t2[i] = x

        # at this point our tensors should be consistent
        # and we can apply the element-wise operation and find the right dimension
        # for the output of the operation
        (t1, t2) = TensorType(tuple(new_t1)), TensorType(tuple(new_t2))
        return (t1, t2)
    else:
        raise TypeError(f'Cannot broadcast types {t1} and {t2}')

def register_inference_rule(call_target):
    def register(fn):
        if call_target in _INFERENCE_RULES:
            raise RuntimeError(f'Inference rule already registered for {call_target}!')
        _INFERENCE_RULES[call_target] = fn
        return fn
    return register

def register_refinement_rule(call_target):
    def register(fn):
        if call_target in _REFINEMENT_RULES:
            raise RuntimeError(f'Refinement rule already registered for {call_target}!')
        _REFINEMENT_RULES[call_target] = fn
        return fn
    return register

def register_algebraic_expressions_inference_rule(call_target):
    def register(fn):
        if call_target in _RULES:
            raise RuntimeError(f'Rule already registered for {call_target}!')
        _RULES[call_target] = fn
        return fn
    return register

@register_inference_rule(torch.add)
@register_inference_rule(operator.add)
def add_inference_rule(n: Node):
    """
    Apply the addition inference rule. This includes:
    - scalar addition
    - broadcasting semantics

    Note that we always return the least precise type between
    the operands (after applying broadcasting) to be the final type of the operation

    Note that we do not modify the operand types themselves after applying broadcasting
    to them. We only use them to calculate the final type
    """
    assert isinstance(n.args[0], Node)
    assert isinstance(n.args[1], Node)
    t1 = n.args[0].type
    t2 = n.args[1].type

    # handle scalar addition
    if t1 == int and isinstance(t2, TensorType):
        n.type = t2
        return n.type

    # handle scalar addition
    elif t2 == int and isinstance(t1, TensorType):
        n.type = t1
        return n.type

    # we bring the new types to the point where
    # we can check for consistency
    # any inconsistency would not have been caused
    # by broadcasting at this point
    (new_t1, new_t2) = broadcast_types(t1, t2)

    if new_t1 != t1 or new_t2 != t2:
        n.meta['broadcast'] = True
        n.meta[str(n.args[0])] = new_t1
        n.meta[str(n.args[1])] = new_t2

    else:
        n.meta['broadcast'] = False

    new_t1 = t1 if not n.meta['broadcast'] else new_t1
    new_t2 = t2 if not n.meta['broadcast'] else new_t2

    # we check for consistency between the new types
    if is_consistent(new_t1, new_t2):
        # we return the less precise type because
        # broadcasting may have happened
        # for operands with shape [1,2,Dyn] and [1,2,1]
        # we have to assign the node [1,2,Dyn]
        if is_more_precise(new_t1, new_t2):
            n.type = new_t2
        else:
            n.type = new_t1
        return n.type
    else:
        raise TypeError(f'Cannot add arguments {n.args[0]} ({ n.args[0].type}) and {n.args[1]} ({ n.args[1].type}) in node {n}.'
                        f' Types should match ')

@register_inference_rule(getattr)
def get_attr_inference_rule(n: Node, traced):
    """
    The current getattr rule only handles the shape attribute
    Can be extended to other attributes
    The most representitive type we have is "Dyn" but the system
    can be extended with more types, such as a type to represent shapes
    """
    attr_node = n.args[0]
    attr_name = n.args[1]

    if attr_name == "shape":
        n.type = Dyn
    else:
        raise TypeError("Not yet implemented")

    # TODO. We leave it like this till we add a type to represent tensor sizes
    return n.type

@register_inference_rule(torch.transpose)
def transpose_inference_rule(n: Node):
    """
    We check that dimensions for the transpose operations
    are within range of the tensor type of the node
    """
    if n.target == torch.transpose:
        assert isinstance(n.args[0], Node)
        t = n.args[0].type

        assert isinstance(n.args[1], int)
        assert isinstance(n.args[2], int)
        dim1, dim2 = n.args[1], n.args[2]

        if t == Dyn:
            n.type = Dyn
            return n.type

        elif isinstance(t, TensorType):
            if 0 <= dim1 < len(t.__args__) and 0 <= dim2 < len(t.__args__):
                new_type = list(t.__args__)
                new_type[dim1], new_type[dim2] = new_type[dim2], new_type[dim1]
                final = TensorType(new_type)
                n.type = get_greatest_upper_bound(n.type, final)
                return n.type
            else:
                raise TypeError(f'Cannot transpose {dim1} and {dim2} in type {t} for node {n}')
        else:
            raise TypeError(f'Cannot transpose {dim1} and {dim2} in type {t} for node {n}')


@register_inference_rule(torch.reshape)
def reshape_inference_rule(n: Node):
    """
    Without dynamism, the rule checks that the
    product of the elements of the argument tensor
    type is equal to the product of the elements
    of the required shape. We gradualize this rule
    by adding a case to handle fully dynamic input
    as well as input where some of the tensor dimensions
    are unknown. In this case we check for divisibility
    """
    assert isinstance(n.args[0], Node)
    t1 = n.args[0].type

    assert isinstance(n.args[1], list)
    t2 = n.args[1]
    t2_type = TensorType([Dyn if elem == -1 else elem for elem in t2])

    # if we do not know the original tensor dimension,
    # we return the required dimension
    if t1 == Dyn:
        n.type = t2_type
        return t2_type

    # if any of the dimensions are unknown,
    # we check for divisibility
    elif isinstance(t1, TensorType):
        assert isinstance(t1, TensorType)
        a = [e if e != Dyn else 1 for e in t1.__args__]
        p1 = reduce(operator.mul, a)
        p2 = reduce(operator.mul, t2)
        if p1 % p2 == 0 or p2 % p1 == 0:
            n.type = t2_type
            return t2_type
        else:
            raise TypeError(f'Cannot reshape in node {n} from {t1} to {t2_type}')
    else:
        raise TypeError(f'Cannot reshape in node {n} from {t1} to {t2_type}')

@register_inference_rule(BatchNorm2d)
def bn2d_inference_rule(n: Node, module_instance):
    """
    Given a BatchNorm2D instance and a node check the following conditions:
    - the input type can be expanded to a size 4 tensor: t =  (x_1, x_2, x_3, x_4)
    - the current node type can be expanded to a size 4 tensor: t' =  (x_1', x_2', x_3', x_4')
    - t is consistent with t'
    - x_2 is consistent with the module's num_features
    - x_2' is consistent with the module's num_features
    output type: the more precise type of t and t'
    """
    assert isinstance(n.args[0], Node)
    n.args[0].type = expand_to_tensor_dim(n.args[0].type, 4)
    arg_type = n.args[0].type
    n.type = expand_to_tensor_dim(n.type, 4)

    # we check the conditions on the incoming argument
    # and any existing annotation
    # we also check for consistency between both annotations
    if is_consistent(arg_type.__args__[1], module_instance.num_features) and \
            is_consistent(n.type.__args__[1], module_instance.num_features) and \
            is_consistent(arg_type, n.type):

        # we choose the more precise type
        # to be the node type
        # so if an incoming argument has more type information
        # we set this node's type to be the argument type
        n.type = get_greatest_upper_bound(arg_type, n.type)
        return n.type
    else:
        raise TypeError(f'Cannot apply {module_instance} with input type {arg_type} and existing type {n.type} on {n}')


def calculate_out_dimension(d_in, module_instance, index):
    """
    For calculating h_in and w_out according to the conv2D documentation
    """
    padding = (module_instance.padding, module_instance.padding) \
        if isinstance(module_instance.padding, int) else module_instance.padding
    kernel_size = (module_instance.kernel_size, module_instance.kernel_size) \
        if isinstance(module_instance.kernel_size, int) else module_instance.kernel_size
    stride = (module_instance.stride, module_instance.stride) \
        if isinstance(module_instance.stride, int) else module_instance.stride
    dilation = (module_instance.dilation, module_instance.dilation) \
        if isinstance(module_instance.dilation, int) else module_instance.dilation

    DIMENSION_TYPES = (int, sympy.Symbol)

    if d_in == Dyn:
        return Dyn

    elif isinstance(d_in, DIMENSION_TYPES):
        n = d_in + 2 * padding[index] - \
            dilation[index] * \
            (kernel_size[index] - 1) - 1

        return (n // stride[0]) + 1

    else:
        raise TypeError(f'{d_in} in {module_instance} must be a number or Dyn. Received {type(d_in)}')


def get_greatest_upper_bound(type1, type2):
    """
    Get the most precise type that's consistent with the given types
    """
    if type1 == Dyn:
        return type2
    elif type2 == Dyn:
        return type1
    elif isinstance(type1, TensorType) and isinstance(type2, TensorType):
        if not is_consistent(type1, type2):
            raise TypeError(f'Inconsistent types {type1}, {type2}')
        gub = [t1 if is_more_precise(t1, t2) else t2 for (t1, t2) in zip(type1.__args__, type2.__args__)]
        return TensorType(tuple(gub))


@register_inference_rule(Conv2d)
def conv2d_inference_rule(n: Node, module_instance):
    """
    Given a Conv2D instance and a node check the following conditions:
    - the input type can be expanded to a size 4 tensor: t =  (x_1, x_2, H, W)
    - the current node type can be expanded to a size 4 tensor: t' =  (x_1', x_2', x_3', x_4')
    - x_2 is consistent with the module's in_channels
    - let o = (x_1, out_channels, H_out, W_out)
    then the output is the greatest upper bound of o and the existing node type t'.
    """
    assert isinstance(n.args[0], Node)
    n.args[0].type = expand_to_tensor_dim(n.args[0].type, 4)
    arg_type = n.args[0].type
    curr_node_type = expand_to_tensor_dim(n.type, 4)

    if is_consistent(arg_type.__args__[1], module_instance.in_channels):
        w_in = arg_type.__args__[3]
        h_in = arg_type.__args__[2]
        h_out = calculate_out_dimension(h_in, module_instance, 0)
        w_out = calculate_out_dimension(w_in, module_instance, 1)
        new_type = TensorType((arg_type.__args__[0], module_instance.out_channels, h_out, w_out))
        gub = get_greatest_upper_bound(new_type, curr_node_type)
        n.type = gub
        return n.type
    else:
        raise TypeError(f'Cannot apply {module_instance} with input type { arg_type} and existing type {n.type} on {n}')


@register_inference_rule(torch.nn.ReLU)
def relu_inference_rule(n: Node, module_instance):
    """
    Input and output shapes should be equal.
    """
    assert isinstance(n.args[0], Node)

    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))

    if isinstance(n.args[0].type, TensorType):
        n.type = get_greatest_upper_bound(n.args[0].type, n.type)
    return n.type


def maxpool2d_check(typ, module_instance):
    """
    Applies the maxpool2d shape information to the input
    this affects the last two dimensions
    """
    new_type_list = list(typ.__args__)
    if len(new_type_list) == 4 or len(new_type_list) == 3:
        w_in = new_type_list[-1]
        h_in = new_type_list[-2]

        h_out = calculate_out_dimension(h_in, module_instance, 0)
        w_out = calculate_out_dimension(w_in, module_instance, 1)

        new_type_list[-1] = w_out
        new_type_list[-2] = h_out
        return TensorType(tuple(new_type_list))

    else:
        raise TypeError(f'Wrong size {typ} for {module_instance}')


@register_inference_rule(torch.nn.MaxPool2d)
def maxpool2d_inference_rule(n: Node, module_instance):
    """
    Given a MaxPool2D instance and a node check the following conditions:
    - Input size matches size 3 or 4
    - Current node type is consistent with the output type we will calculate
    - Input size matches output size and the last two dimensions of the output
      are w_out and h_out. The remaining dimensions are the same as the input
    - Our final result is the greatest upper bound of the output we calculate
      and the current node type.
    """
    assert isinstance(n.args[0], Node)

    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
    if isinstance(n.args[0].type, TensorType):
        output = maxpool2d_check(n.args[0].type, module_instance)
        n.type = get_greatest_upper_bound(output, n.type)
    return n.type



def linear_check(tensor_type, module_instance):
    """
    Checks that an input tensor type satisfies the conditions for linear operation
    and returns the output type based on in and out features given by module_instance
    """
    if len(tensor_type.__args__) >= 2:
        if is_consistent(module_instance.in_features, tensor_type.__args__[-1]):
            new_type_args = list(tensor_type.__args__)
            new_type_args[-1] = module_instance.out_features
            return TensorType(tuple(new_type_args))
        else:
            raise TypeError(f'Inconsistent {module_instance.in_features} and {tensor_type.__args__[-1]} in {module_instance}')
    else:
        raise TypeError(f'Type {tensor_type} must have rank 2 or more.')


@register_inference_rule(torch.nn.Linear)
def linear_inference_rule(n: Node, module_instance):
    """
    Applies the shape information to the input then gets the greatest upper bound
    of the resulting type and the existing type
    """
    assert isinstance(n.args[0], Node)
    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
    if isinstance(n.args[0].type, TensorType):
        output_type = linear_check(n.args[0].type, module_instance)
        n.type = get_greatest_upper_bound(output_type, n.type)
    return n.type


def adaptiveavgpool2d_check(tensor_type, module_instance):
    output_size = module_instance.output_size
    if isinstance(output_size, int):
        output_size = [output_size, output_size]
    elif isinstance(output_size, tuple):
        output_size = list(output_size)
        if output_size[0] is None:
            output_size[0] = output_size[1]
        if output_size[1] is None:
            output_size[1] = output_size[0]

    new_type_list = list(tensor_type.__args__)

    if len(tensor_type.__args__) == 4 or len(tensor_type.__args__) == 3:
        new_type_list[-1] = output_size[1]
        new_type_list[-2] = output_size[0]

        return TensorType(tuple(new_type_list))

    else:
        raise TypeError(f'Tensor ranks must be 3 or 4. Got {tensor_type}')

@register_inference_rule(torch.nn.AdaptiveAvgPool2d)
def adaptiveavgpool2d_inference_rule(n: Node, module_instance):
    """
    The input and output sizes should be the same except for the last
    two dimensions taken from the input, which represent width and height
    """
    assert isinstance(n.args[0], Node)
    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))
    if isinstance(n.args[0].type, TensorType):
        output_type = adaptiveavgpool2d_check(n.args[0].type, module_instance)
        n.type = get_greatest_upper_bound(n.type, output_type)
    return n.type

def flatten_check(tensor_type, start_dim, end_dim):
    l = len(tensor_type.__args__)

    start_dim = l if start_dim == -1 else abs(start_dim)
    end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1

    if 0 <= start_dim <= (l - 1) and 0 <= end_dim <= l and start_dim < end_dim:
        my_args = list(tensor_type.__args__)
        lhs = my_args[0:start_dim]
        rhs = my_args[end_dim:]
        mid = my_args[start_dim:end_dim]
        if Dyn in mid:
            mid = [Dyn]
        else:
            mid = [reduce(operator.mul, my_args[start_dim:end_dim])]
        new_type_list = lhs + mid + rhs
        return TensorType(tuple(new_type_list))
    else:
        raise TypeError(f'Incompatible dimensions {start_dim}, {end_dim - 1} in type {tensor_type}')

@register_inference_rule(torch.flatten)
def flatten_inference_rule(n: Node):
    """
    Applies the flatten shape information to the input then gets the
    greatest upper bound of the resulting type and the existing type
    """
    assert isinstance(n.args[0], Node)

    # set the default start and end dims
    start_dim = 1
    end_dim = -1

    if len(n.args) > 1:
        assert isinstance(n.args[1], int)
        start_dim = n.args[1]

    if len(n.args) > 2:
        assert isinstance(n.args[2], int)
        end_dim = n.args[2]

    if n.args[0].type == Dyn and isinstance(n.type, TensorType):
        n.args[0].type = expand_to_tensor_dim(n.args[0].type, len(n.type.__args__))

    if isinstance(n.args[0].type, TensorType):
        output_type = flatten_check(n.args[0].type, start_dim, end_dim)
        n.type = get_greatest_upper_bound(output_type , n.type)

    return n.type

class GraphTypeChecker:
    def __init__(self, env, traced):
        self.env = env
        self.traced = traced

    def type_check(self):
        """
        A gradual type checker for graphs
        Effect: every node's field type will be
        populated with a type after type-checking is done
        """
        graph = self.traced.graph

        # type check every node with gradual type rules
        # if any node does not type check return false
        for n in graph.nodes:
            self.type_check_node(n)
        return True

    def type_check_node(self, n: Node):
        """
        Type check a given fx node.
        Current operations:
        - Reshape
        - Transpose
        - Add
        - Relu
        - conv2d
        - batchnorm2d
        - flatten
        - maxpool2d
        - adaptiveavgpool2d
        - linear
        """
        if n.type is None:
            n.type = Dyn

        if n.op == 'placeholder':
            return n.type

        elif n.op == 'get_attr':
            t = get_parameter(self.traced, n.target)  # type: ignore[arg-type]
            if isinstance(t.data, torch.Tensor):
                n.type = TensorType(t.data.shape)
            return n.type

        elif n.op == 'call_function':
            if n.target == getattr:
                assert getattr in _INFERENCE_RULES
                return _INFERENCE_RULES[n.target](n, self.traced)

            elif n.target in _INFERENCE_RULES:
                return _INFERENCE_RULES[n.target](n)
            else:
                raise RuntimeError(f'No inference rule registered for target {n.target}!')

        elif n.op == 'call_module':
            module_instance = self.traced.get_submodule(n.target)
            if type(module_instance) in _INFERENCE_RULES:
                return _INFERENCE_RULES[type(module_instance)](n, module_instance)
            else:
                raise RuntimeError(f'No inference rule registered for class {type(module_instance)}!')

        elif n.op == 'output':
            def get_node_type(a):
                return a.type
            n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
            return n.type

        else:
            raise NotImplementedError(f"Method {n.op} not yet implemented")


@register_refinement_rule(Conv2d)
def conv_refinement_rule(n: Node):
    """
    The equality constraints are between the first dimension of
    the input and output
    """
    res = []
    assert isinstance(n.args[0], Node)
    arg_type = n.args[0].type
    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
        res = [Equality(arg_type.__args__[0], n.type.__args__[0])]
        return res


@register_refinement_rule(torch.nn.Linear)
def linear_refinement_rule(n: Node):
    """
    The equality constraints are between the first dimension of
    the input and output
    """
    res = []
    assert isinstance(n.args[0], Node)
    arg_type = n.args[0].type
    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
        res = [Equality(arg_type.__args__[0], n.type.__args__[0])]
    return res

@register_refinement_rule(BatchNorm2d)
@register_refinement_rule(torch.nn.ReLU)
def all_eq(n: Node):
    """
    For operations where the input shape is equal to the output shape
    """
    res = []
    assert isinstance(n.args[0], Node)
    arg_type = n.args[0].type
    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
        args1 = arg_type.__args__
        args2 = n.type.__args__
        res = [Equality(args1[i], args2[i]) for i in range(len(args1))]
    return res


@register_refinement_rule(torch.nn.AdaptiveAvgPool2d)
@register_refinement_rule(torch.nn.MaxPool2d)
def first_two_eq(n: Node):
    """
    For operations where the first two dimensions of the input and output shape
    are equal
    """
    res = []
    assert isinstance(n.args[0], Node)
    arg_type = n.args[0].type
    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
        args1 = arg_type.__args__
        args2 = n.type.__args__
        res = [Equality(args1[0], args2[0]), Equality(args1[1], args2[1])]
    return res


@register_refinement_rule(torch.add)
@register_refinement_rule(operator.add)
def element_wise_eq(n: Node):
    """
    For element-wise operations and handles broadcasting.
    Note that after applying broadcasting to the arguments
    we are able to determine if certain dimensions have not been broadcast
    if they are symbolicallu equal.

    in this case, we can establish equality between those dimensions and the
    corresponding output dimensions.

    Note that it takes two iterations for this result. One iteration to establish
    equality between certain dimensions of the operands (requiring the whole solver
    including unification) and another iteration to establish equality between the operands
    and the resulting type, requiring another round of constraint generation and unificaiton.
    """
    res = []
    if isinstance(n.args[0], Node) and isinstance(n.args[1], Node):
        arg_type1 = n.args[0].type
        arg_type2 = n.args[1].type
        if isinstance(arg_type1, TensorType) and isinstance(arg_type2, TensorType) and isinstance(n.type, TensorType):
            args1, args2 = broadcast_types(arg_type1, arg_type2)
            # by this point, we know that args1 and args2 are the same size.
            a1 = args1.__args__
            a2 = args2.__args__
            a3 = n.type.__args__

            # we would be here in the second iteration where we establish equality
            # between operand type dimensions and the resulting type dimensions
            r = []
            for x, y, z in zip(a1, a2, a3):
                if x == y:
                    r.append(Equality(x, z))
            res = r
    return res


@register_refinement_rule(torch.flatten)
def flatten_refinement_rule(n: Node):
    """
    Generates equality constraints between the dimensions of the input and output
    that will not be involved in the flatten operation
    """
    assert isinstance(n.args[0], Node)

    eq_const = []

    start_dim = 1
    end_dim = -1

    if len(n.args) > 1:
        assert isinstance(n.args[1], int)
        start_dim = n.args[1]

    if len(n.args) > 2:
        assert isinstance(n.args[2], int)
        end_dim = n.args[2]

    if isinstance(n.type, TensorType) and isinstance(n.args[0].type, TensorType):
        l = len(n.type.__args__)
        arg_type = n.args[0].type
        start_dim = l if start_dim == -1 else start_dim
        end_dim = l + end_dim + 1 if end_dim < 0 else end_dim + 1

        for t1, t2 in zip(n.type.__args__[0:start_dim], arg_type.__args__[0:start_dim]):
            eq_const.append(Equality(t1, t2))

        for t1, t2 in zip(n.type.__args__[end_dim:], arg_type.__args__[end_dim:]):
            eq_const.append(Equality(t1, t2))
    return eq_const


@register_algebraic_expressions_inference_rule(Conv2d)
def conv_rule(n: Node, module_instance):
    """
    Represents the outout in terms of an algrbraic expression w.r.t
    the input when possible
    """
    assert isinstance(n.args[0], Node)
    arg_type = n.args[0].type
    if isinstance(arg_type, TensorType) and isinstance(n.type, TensorType):
        w_in = arg_type.__args__[3]
        h_in = arg_type.__args__[2]
        h_out = calculate_out_dimension(h_in, module_instance, 0)
        w_out = calculate_out_dimension(w_in, module_instance, 1)
        new_type = TensorType((n.type.__args__[0], n.type.__args__[1], h_out, w_out))
        n.type = new_type
        return new_type

class Refine:
    """
    Symbolic shape inference.
    Generates constraints over type variables.
    Currently all constraints are equality constraints.
    """
    def __init__(self, traced):
        self.constraints = []
        self.traced = traced
        self.symbol_iter = itertools.count(start=0, step=1)

    def refine(self):
        """
        Generates constraints for
        every node in the graph based on
        the operation.
        """
        graph = self.traced.graph
        for n in graph.nodes:
            self.refine_node(n)
        return True

    def symbolic_relations(self):
        """
        Infers algebraic relations
        """
        graph = self.traced.graph
        for n in graph.nodes:
            self.infer_symbolic_relations(n)
        return True

    def replace_dyn_with_fresh_var(self, typ):
        """
        Replace all unknown types with fresh type variables.
        """
        if typ == Dyn:
            new_symbol = Var(next(self.symbol_iter))
            return new_symbol
        elif isinstance(typ, TensorType):
            new_args = [self.replace_dyn_with_fresh_var(a) for a in typ.__args__]
            return TensorType(tuple(new_args))
        elif isinstance(typ, list):
            return [self.replace_dyn_with_fresh_var(t) for t in typ]
        elif isinstance(typ, tuple):
            return (self.replace_dyn_with_fresh_var(t) for t in typ)
        else:
            return typ


    def convert_to_sympy_symbols(self, typ):
        """
        Replace all unknown types with fresh type variables.
        """
        if isinstance(typ, Var):
            return sympy.symbols(str(typ))
        elif isinstance(typ, TensorType):
            new_args = [self.convert_to_sympy_symbols(a) for a in typ.__args__]
            return TensorType(tuple(new_args))
        elif isinstance(typ, list):
            return [self.convert_to_sympy_symbols(t) for t in typ]
        elif isinstance(typ, tuple):
            return (self.convert_to_sympy_symbols(t) for t in typ)
        else:
            return typ

    def refine_node(self, n: Node):
        """
        Returns a list of equality constraints for
        call_module and call_function nodes.
        Models the relation between input and output dimensions
        using constraints in case they are both tensors.
        All operations used in resnet50 are defined.
        """
        if n.type is None:
            n.type = Dyn

        n.type = self.replace_dyn_with_fresh_var(n.type)

        if n.op == 'call_function':
            if n.target in _REFINEMENT_RULES:
                self.constraints += _REFINEMENT_RULES[n.target](n)
            else:
                pass

        if n.op == 'call_module':
            module_instance = self.traced.get_submodule(n.target)
            if type(module_instance) in _REFINEMENT_RULES:
                self.constraints += _REFINEMENT_RULES[type(module_instance)](n)
            else:
                pass

        if n.op == 'output':
            def get_node_type(a):
                return a.type
            n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
            return n.type

        else:
            pass

    def infer_symbolic_relations(self, n: Node):
        n.type = self.convert_to_sympy_symbols(n.type)
        if n.op == 'call_function':
            if n.target in _RULES:
                return _RULES[n.target](n)
            else:
                pass

        if n.op == 'call_module':
            module_instance = self.traced.get_submodule(n.target)
            if type(module_instance) in _RULES:
                return _RULES[type(module_instance)](n, module_instance)
            else:
                pass

        if n.op == 'output':
            def get_node_type(a):
                return a.type
            n.type = torch.fx.node.map_arg(n.args[0], get_node_type)
            return n.type

        else:
            pass

def get_parameter(traced, target: str):
    """
    Returns the parameter given by ``target`` if it exists,
    otherwise throws an error.

    See the docstring for ``get_submodule`` for a more detailed
    explanation of this method's functionality as well as how to
    correctly specify ``target``.

    Args:
        target: The fully-qualified string name of the Parameter
            to look for. (See ``get_submodule`` for how to specify a
            fully-qualified string.)

    Returns:
        torch.nn.Parameter: The Parameter referenced by ``target``

    Raises:
        AttributeError: If the target string references an invalid
            path or resolves to something that is not an
            ``nn.Parameter``
    """
    module_path, _, param_name = target.rpartition(".")

    mod: torch.nn.Module = traced.get_submodule(module_path)

    if not hasattr(mod, param_name):
        raise AttributeError(mod._get_name() + " has no attribute `" + param_name + "`")

    param: torch.nn.Parameter = getattr(mod, param_name)

    return param
