# Copyright The PyTorch Lightning team.
#
# 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.
"""Helper functions to help with reproducibility of models. """

import logging
import os
import random
from typing import Optional

import numpy as np
import torch

from pytorch_lightning.utilities import rank_zero_warn

log = logging.getLogger(__name__)


def seed_everything(seed: Optional[int] = None) -> int:
    """
    Function that sets seed for pseudo-random number generators in:
    pytorch, numpy, python.random
    In addition, sets the env variable `PL_GLOBAL_SEED` which will be passed to
    spawned subprocesses (e.g. ddp_spawn backend).

    Args:
        seed: the integer value seed for global random state in Lightning.
            If `None`, will read seed from `PL_GLOBAL_SEED` env variable
            or select it randomly.
    """
    max_seed_value = np.iinfo(np.uint32).max
    min_seed_value = np.iinfo(np.uint32).min

    try:
        if seed is None:
            seed = os.environ.get("PL_GLOBAL_SEED")
        seed = int(seed)
    except (TypeError, ValueError):
        seed = _select_seed_randomly(min_seed_value, max_seed_value)
        rank_zero_warn(f"No correct seed found, seed set to {seed}")

    if not (min_seed_value <= seed <= max_seed_value):
        rank_zero_warn(f"{seed} is not in bounds, numpy accepts from {min_seed_value} to {max_seed_value}")
        seed = _select_seed_randomly(min_seed_value, max_seed_value)

    log.info(f"Global seed set to {seed}")
    os.environ["PL_GLOBAL_SEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    return seed


def _select_seed_randomly(min_seed_value: int = 0, max_seed_value: int = 255) -> int:
    return random.randint(min_seed_value, max_seed_value)

# Copyright The PyTorch Lightning team.
#
# 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.
"""Helper functions to help with reproducibility of models. """

import logging
import os
import random
from typing import Optional

import numpy as np
import torch

from pytorch_lightning.utilities import rank_zero_warn

log = logging.getLogger(__name__)


def seed_everything(seed: Optional[int] = None) -> int:
    """
    Function that sets seed for pseudo-random number generators in:
    pytorch, numpy, python.random
    In addition, sets the env variable `PL_GLOBAL_SEED` which will be passed to
    spawned subprocesses (e.g. ddp_spawn backend).

    Args:
        seed: the integer value seed for global random state in Lightning.
            If `None`, will read seed from `PL_GLOBAL_SEED` env variable
            or select it randomly.
    """
    max_seed_value = np.iinfo(np.uint32).max
    min_seed_value = np.iinfo(np.uint32).min

    try:
        if seed is None:
            seed = os.environ.get("PL_GLOBAL_SEED")
        seed = int(seed)
    except (TypeError, ValueError):
        seed = _select_seed_randomly(min_seed_value, max_seed_value)
        rank_zero_warn(f"No correct seed found, seed set to {seed}")

    if not (min_seed_value <= seed <= max_seed_value):
        rank_zero_warn(f"{seed} is not in bounds, numpy accepts from {min_seed_value} to {max_seed_value}")
        seed = _select_seed_randomly(min_seed_value, max_seed_value)

    # using `log.info` instead of `rank_zero_info`,
    # so users can verify the seed is properly set in distributed training.
    log.info(f"Global seed set to {seed}")
    os.environ["PL_GLOBAL_SEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    return seed


def _select_seed_randomly(min_seed_value: int = 0, max_seed_value: int = 255) -> int:
    return random.randint(min_seed_value, max_seed_value)

Update seed_everything() (#6843)