# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
import paddle

__all__ = ["einsum", "transfer_param"]


def einsum(equation, *operands):
    r"""
    Executes the sum of product of provided operands based on the Einstein summation convention.
    Einsum can be used to complete a variety of operations, such as sum, transpose,
    batch matrix multiplication.

    Args:
        equation (`str`):
            Uses uncased letters to specify the dimension of the operands and result. The input
            equation is on the left hand before `->` while the output equation is on the right side.
            Einsum can infer the result shape so that the `->` and the result label letters can be omitted.
            Operands in the input equation are splitted by commas (','), e.g. 'abc,cde' describes two 3D
            operands. The dimensions labeled with same letter should be same or be 1. Ellipsis ('...') can
            be used to specify the broadcast dimensions.

        operands (`Tensor`):
            The operands to compute the Einstein sum of. The number of operands should be the same as the
            the operands described in input equation.

    Returns:
        `Tensor`: The result of Einstein sum product.

    Example:
        .. code-block::

            import numpy as np
            import paddle
            import paddlenlp

            np.random.seed(102)

            x = paddle.to_tensor(np.random.rand(4))
            y = paddle.to_tensor(np.random.rand(5))
            # sum
            print(paddlenlp.ops.einsum('i->', x))
            # Tensor(shape=[], dtype=float64, place=CUDAPlace(0), stop_gradient=True, 2.30369050)

            # dot
            print(paddlenlp.ops.einsum('i,i->', x, x))
            # Tensor(shape=[], dtype=float64, place=CUDAPlace(0), stop_gradient=True, 1.43773247)

            # outer
            print(paddlenlp.ops.einsum("i,j->ij", x, y)),
            # Tensor(shape=[4, 5], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #         [[0.34590188, 0.48353496, 0.09996135, 0.18656330, 0.21392910],
            #         [0.39122025, 0.54688535, 0.11305780, 0.21100591, 0.24195704],
            #         [0.17320613, 0.24212422, 0.05005442, 0.09341929, 0.10712238],
            #         [0.42290818, 0.59118179, 0.12221522, 0.22809690, 0.26155500]])

            A = paddle.to_tensor(np.random.rand(2, 3, 2))
            B = paddle.to_tensor(np.random.rand(2, 2, 3))
            # transpose
            print(paddlenlp.ops.einsum('ijk->kji', A))
            #  Tensor(shape=[2, 3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #        [[[0.49174730, 0.33344683],
            #          [0.89440989, 0.26162022],
            #          [0.36116209, 0.12241719]],

            #         [[0.49019824, 0.51895050],
            #          [0.18241053, 0.13092809],
            #          [0.81059146, 0.55165734]]])

            # batch matrix multiplication
            print(paddlenlp.ops.einsum('ijk, ikl->ijl', A,B))
            # Tensor(shape=[2, 3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #     [[[0.13654339, 0.39331432, 0.65059661],
            #      [0.07171420, 0.57518653, 0.77629221],
            #      [0.21250688, 0.37793541, 0.73643411]],

            #     [[0.56925339, 0.65859030, 0.57509818],
            #      [0.30368265, 0.25778348, 0.21630400],
            #      [0.39587265, 0.58031243, 0.51824755]]])

            # Ellipsis transpose
            print(paddlenlp.ops.einsum('...jk->...kj', A))
            # Tensor(shape=[2, 2, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            #     [[[0.49174730, 0.89440989, 0.36116209],
            #         [0.49019824, 0.18241053, 0.81059146]],

            #         [[0.33344683, 0.26162022, 0.12241719],
            #         [0.51895050, 0.13092809, 0.55165734]]])

            # Ellipsis batch matrix multiplication
            print(paddlenlp.ops.einsum('...jk, ...kl->...jl', A,B))
            # Tensor(shape=[2, 3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
            # [[[0.13654339, 0.39331432, 0.65059661],
            #     [0.07171420, 0.57518653, 0.77629221],
            #     [0.21250688, 0.37793541, 0.73643411]],

            #     [[0.56925339, 0.65859030, 0.57509818],
            #     [0.30368265, 0.25778348, 0.21630400],
            #     [0.39587265, 0.58031243, 0.51824755]]])
    """
    # paddle.einsum can be used in paddle 2.3.0+
    if hasattr(paddle, "einsum"):
        return paddle.einsum(equation, *operands)

    def _mul_sum(left, right, sum_dims):
        assert left.rank() == right.rank(), "number of rank should be equal."
        if len(sum_dims) == 0:
            return left * right
        sum_dims_set = set(sum_dims)
        batch_dims = []
        left_out_dims = []
        right_out_dims = []
        batch_size = summed_size = left_size = right_size = 1
        dim = len(left.shape)
        for i in range(dim):
            is_left_summed_dim = left.shape[i] > 1  # not broadcast dim
            is_right_summed_dim = right.shape[i] > 1
            if i in sum_dims_set:
                if is_left_summed_dim and is_right_summed_dim:
                    assert left.shape[i] == right.shape[i], "Non-broadcast dim should be equal."
                    summed_size *= left.shape[i]
                elif is_left_summed_dim:
                    left = left.sum(axis=i, keepdim=True)
                elif is_right_summed_dim:
                    right = right.sum(axis=i, keepdim=True)
            elif is_left_summed_dim and is_right_summed_dim:
                assert left.shape[i] == right.shape[i], "Non-broadcast dim should be equal."
                batch_dims.append(i)
                batch_size *= left.shape[i]
            elif is_left_summed_dim:
                left_out_dims.append(i)
                left_size *= left.shape[i]
            else:
                right_out_dims.append(i)
                right_size *= right.shape[i]
        out_shape = [left.shape[i] for i in batch_dims + left_out_dims]
        out_shape.extend([1] * len(sum_dims))
        out_shape.extend([right.shape[i] for i in right_out_dims])

        left_perm = list(batch_dims)
        left_perm.extend(left_out_dims)
        left_perm.extend(sum_dims)
        left_perm.extend(right_out_dims)

        right_perm = list(batch_dims)
        right_perm.extend(sum_dims)
        right_perm.extend(right_out_dims)
        right_perm.extend(left_out_dims)

        output_perm = [-1] * (len(batch_dims) + len(left_out_dims) + len(sum_dims) + len(right_out_dims))
        for i, j in enumerate(batch_dims + left_out_dims + sum_dims + right_out_dims):
            output_perm[j] = i

        left = paddle.reshape(paddle.transpose(left, perm=left_perm), (batch_size, left_size, summed_size))
        right = paddle.reshape(paddle.transpose(right, perm=right_perm), (batch_size, summed_size, right_size))
        result = paddle.matmul(left, right)
        result = paddle.reshape(result, out_shape)
        result = paddle.transpose(result, output_perm)
        return result

    if len(operands) == 1 and isinstance(operands[0], (list, tuple)):
        operands = operands[0]
    # Equation is case insensitive
    num_letters = 26
    letters_to_idx = [-1] * num_letters
    equation = equation.lower().replace(" ", "")
    # 1. Parse the equation
    eqns = equation.split("->")
    num_eqns_size = len(eqns)
    assert num_eqns_size <= 2, "The '->' should exist at most only once"

    input_eqn = eqns[0]
    output_eqn = None if num_eqns_size <= 1 else eqns[1]
    operand_eqns = input_eqn.split(",")
    assert len(operand_eqns) == len(
        operands
    ), "Number of operands in equation and the tensors provided should be equal."

    # Parse input equation
    num_total_idxes = 0
    input_operand_idxes = []
    letter_frequence = [0] * num_letters
    idxes_last_operand = []
    num_ell_idxes = -1
    first_ell_idx = 0
    for i, term in enumerate(operand_eqns):
        ell_char_count = 0
        operand_rank = int(operands[i].rank().cpu().numpy())
        curr_num_ell_idxes = operand_rank - len(term) + 3
        dims_in_terms = 0
        curr_operand_idxes = []
        for ch in term:
            if ch == ".":
                ell_char_count += 1
                assert ell_char_count <= 3, "The '.' should only exist in one ellipsis '...' in term {}".format(term)
                if ell_char_count == 3:
                    if num_ell_idxes == -1:
                        num_ell_idxes = curr_num_ell_idxes
                        first_ell_idx = num_total_idxes
                        num_total_idxes += num_ell_idxes
                    else:
                        assert (
                            curr_num_ell_idxes == num_ell_idxes
                        ), "Ellipsis in all terms should represent same dimensions ({}).".format(num_ell_idxes)

                    for j in range(num_ell_idxes):
                        curr_operand_idxes.append(j + first_ell_idx)
                        idxes_last_operand.append(i)
                    dims_in_terms += num_ell_idxes
            else:
                assert (ell_char_count == 0) or (
                    ell_char_count == 3
                ), "'.' must only occur in ellipsis, operand {}".format(term)
                assert ord("a") <= ord(ch) and ord(ch) <= ord("z"), "only accept alphabet (a-zA-Z)"
                letter_num = ord(ch) - ord("a")
                if letters_to_idx[letter_num] == -1:
                    letters_to_idx[letter_num] = num_total_idxes
                    num_total_idxes += 1
                    idxes_last_operand.append(i)
                else:
                    idxes_last_operand[letters_to_idx[letter_num]] = i
                letter_frequence[letter_num] += 1
                curr_operand_idxes.append(letters_to_idx[letter_num])
                dims_in_terms += 1

        assert dims_in_terms == operand_rank, "Dimension dismatch for operand {}: equation {}, tensor {}".format(
            i, dims_in_terms, operand_rank
        )
        input_operand_idxes.append(curr_operand_idxes)
    # Parse output equation
    idxes_to_output_dims = [-1] * num_total_idxes
    num_output_dims = 0
    if num_eqns_size == 2:
        ell_char_count = 0
        for ch in output_eqn:
            if ch == ".":
                ell_char_count += 1
                assert ell_char_count <= 3, "The '.' should only exist in one ellipsis '...' in term {}".format(
                    output_eqn
                )
                if ell_char_count == 3:
                    assert num_ell_idxes > -1, "Input equation '{}' don't have ellipsis.".format(input_eqn)
                    for j in range(num_ell_idxes):
                        idxes_to_output_dims[first_ell_idx + j] = num_output_dims
                        num_output_dims += 1

            else:
                assert (ell_char_count == 0) or (
                    ell_char_count == 3
                ), "'.' must only occur in ellipsis, operand {}".format(output_eqn)
                assert ord("a") <= ord(ch) and ord(ch) <= ord("z"), "only accept alphabet (a-zA-Z)"
                letter_num = ord(ch) - ord("a")
                assert letters_to_idx[letter_num] != -1, "character {} doesn't exist in input".format(ch)
                assert (
                    idxes_to_output_dims[letters_to_idx[letter_num]] == -1
                ), "character {} occurs twice in output".format(ch)

                idxes_to_output_dims[letters_to_idx[letter_num]] = num_output_dims
                num_output_dims += 1
    else:  # num_eqns_size == 1
        # Infer the output dims
        if num_ell_idxes >= 0:
            for j in range(num_ell_idxes):
                idxes_to_output_dims[first_ell_idx + j] = num_output_dims
                num_output_dims += 1
        for j in range(num_letters):
            if letter_frequence[j] == 1:
                idxes_to_output_dims[letters_to_idx[j]] = num_output_dims
                num_output_dims += 1

    # Mark sum index
    sum_dim = num_output_dims
    for i in range(num_total_idxes):
        if idxes_to_output_dims[i] == -1:
            idxes_to_output_dims[i] = sum_dim
            sum_dim += 1

    preprocessed_operands = []
    size_dims = [-1] * num_total_idxes
    for i, preprocessed_operand in enumerate(operands):
        idx_to_dims = [-1] * num_total_idxes
        curr_operand_idxes = input_operand_idxes[i]
        dim = 0
        for j, idx in enumerate(curr_operand_idxes):
            output_dim = idxes_to_output_dims[idx]
            if idx_to_dims[output_dim] == -1:
                idx_to_dims[output_dim] = dim
                if size_dims[idx] == -1:
                    size_dims[idx] = preprocessed_operand.shape[dim]
                else:
                    assert (
                        size_dims[idx] == preprocessed_operand.shape[dim]
                    ), "Dimension size does not match previous size. "
                dim += 1
            else:
                # Diagonal repeated index
                # TODO(zhoushunjie): Need to develop a paddle.diagonal api
                raise NotImplementedError("Can't support diagonal.")
        perm = []
        for input_dim in idx_to_dims:
            if input_dim > -1:
                perm.append(input_dim)
        # Transpose the tensor by perm
        preprocessed_operand = paddle.transpose(preprocessed_operand, perm=perm)

        for dim, input_dim in enumerate(idx_to_dims):
            if input_dim == -1:
                preprocessed_operand = paddle.unsqueeze(preprocessed_operand, dim)

        preprocessed_operands.append(preprocessed_operand)

    # 2. Execute the mul_sum
    sum_dims = []
    result = preprocessed_operands[0]
    for i in range(num_total_idxes):
        if idxes_last_operand[i] == 0 and idxes_to_output_dims[i] >= num_output_dims:
            result = result.sum(axis=idxes_to_output_dims[i], keepdim=True)
    for i in range(1, len(preprocessed_operands)):
        for j in range(num_total_idxes):
            if idxes_last_operand[j] == i and idxes_to_output_dims[j] >= num_output_dims:
                sum_dims.append(idxes_to_output_dims[j])
        result = _mul_sum(result, preprocessed_operands[i], sum_dims)

    squeeze_dims = [i for i in range(len(result.shape) - 1, num_output_dims - 1, -1)]
    if len(squeeze_dims) != 0:
        result = paddle.squeeze(result, squeeze_dims)
    return result


# copy from fast transformers
def transfer_param(p, is_bias=False, dtype="float16", restore_data=False):
    param_shape = p.shape
    # Allow CPU/GPU and float16/float32 transfer
    # NOTE: str(p.place) differs between paddle develop and 2.2
    if str(p.dtype)[-len(dtype) :] == dtype and ("gpu" in str(p.place).lower() or "cuda" in str(p.place).lower()):
        return p
    if restore_data:
        if paddle.in_dynamic_mode():
            param_data = p.numpy()
            # Creating parameters with Assign initializer is too slow. Maybe we
            # can cast to fp16 directly and get a tensor, while we do it more
            # elaborately to get a ParamBase. Also note `VarBase.set_value`
            # enforce the same dtype and can not be used directly.
            new_p = type(p)(shape=param_shape, dtype=dtype, is_bias=is_bias)
            new_p.value().get_tensor().set(param_data.astype(dtype), paddle.framework._current_expected_place())
            return new_p
        else:
            param_data = np.array(paddle.static.global_scope().find_var(p.name).get_tensor())
    return paddle.create_parameter(
        shape=param_shape,
        dtype=dtype,
        is_bias=is_bias,
        default_initializer=paddle.nn.initializer.Assign(param_data) if restore_data else None,
    )
