#!/usr/bin/env python
# -*- encoding: utf-8 -*-

import math
import os

import torch.distributed as dist

from colossalai.constants import TESSERACT_DIM, TESSERACT_DEP
from colossalai.context import Config
from colossalai.registry import DIST_GROUP_INITIALIZER
from .process_group_initializer import ProcessGroupInitializer
from ..parallel_mode import ParallelMode


def _check_tesseract_env_var(tesseract_dim: int,
                             tesseract_dep: int):
    # check environment variable for TESSERACT
    env_tesseract_dim = os.environ.get(TESSERACT_DIM, None)
    env_tesseract_dep = os.environ.get(TESSERACT_DEP, None)

    if env_tesseract_dim and env_tesseract_dep:
        assert int(env_tesseract_dim) == tesseract_dim, \
            'TESSERACT_DIM has been set in the current environment and ' \
            'does not match with the value passed to this initialized'
        assert int(env_tesseract_dep) == tesseract_dep, \
            'TESSERACT_DEP has been set in the current environment and ' \
            'does not match with the value passed to this initialized'
    else:
        os.environ[TESSERACT_DIM] = str(tesseract_dim)
        os.environ[TESSERACT_DEP] = str(tesseract_dep)


# i row j col k dep
class Initializer_2p5D_ROW(ProcessGroupInitializer):
    '''2p5d tensor parallel initialization among rows. 
    '''

    def __init__(self,
                 tesseract_dim: int,
                 tesseract_dep: int,
                 *args):
        super(Initializer_2p5D_ROW, self).__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dep = tesseract_dep
        self.tesseract_dim = tesseract_dim
        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"

    def init_dist_group(self):
        '''Initialize 2p5D tensor row parallel groups, and assign local_ranks and groups to each gpu.

        :return: 2p5D tensor row parallelism's information 
        :rtype: tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
        '''
        local_rank = None
        ranks_in_group = None
        process_group = None
        group_world_size = None
        mode = ParallelMode.PARALLEL_2P5D_ROW

        for h in range(self.num_group):
            for j in range(self.tesseract_dim):
                for k in range(self.tesseract_dep):
                    ranks = [h * self.tensor_parallel_size + i + self.tesseract_dim * (
                        j + self.tesseract_dim * k) for i in range(self.tesseract_dim)]
                    group = dist.new_group(ranks)

                    if self.rank in ranks:
                        local_rank = ranks.index(self.rank)
                        group_world_size = len(ranks)
                        process_group = group
                        ranks_in_group = ranks

        return local_rank, group_world_size, process_group, ranks_in_group, mode


class Initializer_2p5D_Col(ProcessGroupInitializer):
    '''2p5d tensor parallel initialization among cols. 
    '''

    def __init__(self,
                 tesseract_dim: int,
                 tesseract_dep: int,
                 *args):
        super(Initializer_2p5D_Col, self).__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dep = tesseract_dep
        self.tesseract_dim = tesseract_dim
        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"

    def init_dist_group(self):
        '''Initialize 2p5D tensor col parallel groups, and assign local_ranks and groups to each gpu.

        :return: 2p5D tensor col parallelism's information 
        :rtype: tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
        '''
        local_rank = None
        ranks_in_group = None
        process_group = None
        group_world_size = None
        mode = ParallelMode.PARALLEL_2P5D_COL

        for h in range(self.num_group):
            for i in range(self.tesseract_dim):
                for k in range(self.tesseract_dep):
                    ranks = [h * self.tensor_parallel_size + i + self.tesseract_dim * (
                        j + self.tesseract_dim * k) for j in range(self.tesseract_dim)]
                    group = dist.new_group(ranks)

                    if self.rank in ranks:
                        local_rank = ranks.index(self.rank)
                        group_world_size = len(ranks)
                        process_group = group
                        ranks_in_group = ranks

        return local_rank, group_world_size, process_group, ranks_in_group, mode


class Initializer_2p5D_Dep(ProcessGroupInitializer):
    '''2p5D tensor parallel initialization among depths. 
    '''

    def __init__(self,
                 tesseract_dim: int,
                 tesseract_dep: int,
                 *args):
        super(Initializer_2p5D_Dep, self).__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dep = tesseract_dep
        self.tesseract_dim = tesseract_dim
        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"

    def init_dist_group(self):
        '''Initialize 2p5D tensor depth parallel groups, and assign local_ranks and groups to each gpu.

        :return: 2p5D tensor depth parallelism's information 
        :rtype: tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
        '''
        local_rank = None
        ranks_in_group = None
        process_group = None
        group_world_size = None
        mode = ParallelMode.PARALLEL_2P5D_DEP

        for h in range(self.num_group):
            for i in range(self.tesseract_dim):
                for j in range(self.tesseract_dim):
                    ranks = [h * self.tensor_parallel_size + i + self.tesseract_dim * (
                        j + self.tesseract_dim * k) for k in range(self.tesseract_dep)]
                    group = dist.new_group(ranks)

                    if self.rank in ranks:
                        local_rank = ranks.index(self.rank)
                        group_world_size = len(ranks)
                        process_group = group
                        ranks_in_group = ranks

        return local_rank, group_world_size, process_group, ranks_in_group, mode


# i row j col k dep
class Initializer_2p5D_XZ(ProcessGroupInitializer):
    '''2p5d tensor parallel initialization among cols times dep. 
    '''

    def __init__(self,
                 tesseract_dim: int,
                 tesseract_dep: int,
                 *args):
        super(Initializer_2p5D_XZ, self).__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dep = tesseract_dep
        self.tesseract_dim = tesseract_dim
        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "Tensor parallel size should be depth * dim ** 2 in 2.5D parallel"

    def init_dist_group(self):
        '''Initialize 2p5D tensor colXdepth parallel groups, and assign local_ranks and groups to each gpu.

        :return: 2p5D tensor colXdepth parallelism's information 
        :rtype: tuple(local_rank, group_world_size, process_group, ranks_in_group, mode)
        '''
        local_rank = None
        ranks_in_group = None
        process_group = None
        group_world_size = None
        mode = ParallelMode.PARALLEL_2P5D_XZ

        for h in range(self.num_group):
            for i in range(self.tesseract_dim):
                ranks = [h * self.tensor_parallel_size + i + self.tesseract_dim * (
                    j + self.tesseract_dim * k) for k in range(self.tesseract_dep) for j in
                    range(self.tesseract_dim)]
                group = dist.new_group(ranks)

                if self.rank in ranks:
                    local_rank = ranks.index(self.rank)
                    group_world_size = len(ranks)
                    process_group = group
                    ranks_in_group = ranks

        return local_rank, group_world_size, process_group, ranks_in_group, mode


@DIST_GROUP_INITIALIZER.register_module
class Initializer_2p5D(ProcessGroupInitializer):
    """
    Serve as the single entry point to Tesseract parallel initialization.
    """

    def __init__(self,
                 rank: int,
                 world_size: int,
                 config: Config,
                 data_parallel_size: int,
                 pipeline_parlalel_size: int,
                 tensor_parallel_size: int,
                 depth: int
                 ):
        args = (rank, world_size, config, data_parallel_size, pipeline_parlalel_size, tensor_parallel_size)
        super().__init__(*args)
        self.num_group = self.world_size // self.tensor_parallel_size
        self.tesseract_dim = int(math.sqrt(self.tensor_parallel_size / depth))
        self.tesseract_dep = depth

        assert self.tensor_parallel_size == self.tesseract_dim ** 2 * self.tesseract_dep, \
            "2.5D tesseract dim should equal to (tensor parallel size / tesseract dep) ^ 0.5"
        _check_tesseract_env_var(self.tesseract_dim, self.tesseract_dep)

        self.col_initializer = Initializer_2p5D_Col(self.tesseract_dim, self.tesseract_dep, *args)
        self.row_initializer = Initializer_2p5D_ROW(self.tesseract_dim, self.tesseract_dep, *args)
        self.dep_initializer = Initializer_2p5D_Dep(self.tesseract_dim, self.tesseract_dep, *args)
        self.xz_initializer = Initializer_2p5D_XZ(self.tesseract_dim, self.tesseract_dep, *args)

    def init_dist_group(self):
        '''Initialize 2p5D tensor row, col, depth, and colXdepth parallel groups, and assign local_ranks and groups to each gpu.

        :return: Whole 2p5D tensor parallelism's information
        :rtype: list of tuples (local_rank, group_world_size, process_group, ranks_in_group, mode)
        '''
        parallel_setting = []
        parallel_setting.append(self.col_initializer.init_dist_group())
        parallel_setting.append(self.row_initializer.init_dist_group())
        parallel_setting.append(self.dep_initializer.init_dist_group())
        parallel_setting.append(self.xz_initializer.init_dist_group())
        return parallel_setting