Source code for

import copy
import logging
import re
from import Mapping
from typing import Any, Dict, Generator, Iterable, List, Optional, Tuple

import numpy
import random

from import Categorical, Domain, Function, RandomState
from ray.util.annotations import DeveloperAPI, PublicAPI

logger = logging.getLogger(__name__)

def generate_variants(
    unresolved_spec: Dict,
    constant_grid_search: bool = False,
    random_state: "RandomState" = None,
) -> Generator[Tuple[Dict, Dict], None, None]:
    """Generates variants from a spec (dict) with unresolved values.

    There are two types of unresolved values:

        Grid search: These define a grid search over values. For example, the
        following grid search values in a spec will produce six distinct
        variants in combination:

            "activation": grid_search(["relu", "tanh"])
            "learning_rate": grid_search([1e-3, 1e-4, 1e-5])

        Lambda functions: These are evaluated to produce a concrete value, and
        can express dependencies or conditional distributions between values.
        They can also be used to express random search (e.g., by calling
        into the `random` or `np` module).

            "cpu": lambda spec: spec.config.num_workers
            "batch_size": lambda spec: random.uniform(1, 1000)

    Finally, to support defining specs in plain JSON / YAML, grid search
    and lambda functions can also be defined alternatively as follows:

        "activation": {"grid_search": ["relu", "tanh"]}
        "cpu": {"eval": "spec.config.num_workers"}

    Use `format_vars` to format the returned dict of hyperparameters.

        (Dict of resolved variables, Spec object)
    for resolved_vars, spec in _generate_variants_internal(
        assert not _unresolved_values(spec)
        yield resolved_vars, spec

    "random": random,
    "np": numpy,


def _resolve_nested_dict(nested_dict: Dict) -> Dict[Tuple, Any]:
    """Flattens a nested dict by joining keys into tuple of paths.

    Can then be passed into `format_vars`.
    res = {}
    for k, v in nested_dict.items():
        if isinstance(v, dict):
            for k_, v_ in _resolve_nested_dict(v).items():
                res[(k,) + k_] = v_
            res[(k,)] = v
    return res

def format_vars(resolved_vars: Dict) -> str:
    """Format variables to be used as experiment tags.

    Experiment tags are used in directory names, so this method makes sure
    the resulting tags can be legally used in directory names on all systems.

    The input to this function is a dict of the form
    ``{("nested", "config", "path"): "value"}``. The output will be a comma
    separated string of the form ``last_key=value``, so in this example

    Note that the sanitizing implies that empty strings are possible return
    values. This is expected and acceptable, as it is not a common case and
    the resulting directory names will still be valid.

        resolved_vars: Dictionary mapping from config path tuples to a value.

        Comma-separated key=value string.
    vars = resolved_vars.copy()
    # TrialRunner already has these in the experiment_tag
    for v in ["run", "env", "resources_per_trial"]:
        vars.pop(v, None)

    return ",".join(
        f"{_clean_value(k[-1])}={_clean_value(v)}" for k, v in sorted(vars.items())

def _flatten_resolved_vars(resolved_vars: Dict) -> Dict:
    """Formats the resolved variable dict into a mapping of (str -> value)."""
    flattened_resolved_vars_dict = {}
    for pieces, value in resolved_vars.items():
        if pieces[0] == "config":
            pieces = pieces[1:]
        pieces = [str(piece) for piece in pieces]
        flattened_resolved_vars_dict["/".join(pieces)] = value
    return flattened_resolved_vars_dict

def _clean_value(value: Any) -> str:
    """Format floats and replace invalid string characters with ``_``."""
    if isinstance(value, float):
        return f"{value:.4f}"
        # Define an invalid alphabet, which is the inverse of the
        # stated regex characters
        invalid_alphabet = r"[^a-zA-Z0-9_-]+"
        return re.sub(invalid_alphabet, "_", str(value)).strip("_")

def parse_spec_vars(
    spec: Dict,
) -> Tuple[List[Tuple[Tuple, Any]], List[Tuple[Tuple, Any]], List[Tuple[Tuple, Any]]]:
    resolved, unresolved = _split_resolved_unresolved_values(spec)
    resolved_vars = list(resolved.items())

    if not unresolved:
        return resolved_vars, [], []

    grid_vars = []
    domain_vars = []
    for path, value in unresolved.items():
        if value.is_grid():
            grid_vars.append((path, value))
            domain_vars.append((path, value))

    return resolved_vars, domain_vars, grid_vars

def _count_spec_samples(spec: Dict, num_samples=1) -> int:
    """Count samples for a specific spec"""
    _, domain_vars, grid_vars = parse_spec_vars(spec)
    grid_count = 1
    for path, domain in grid_vars:
        grid_count *= len(domain.categories)
    return num_samples * grid_count

def _count_variants(spec: Dict, presets: Optional[List[Dict]] = None) -> int:
    # Helper function: Deep update dictionary
    def deep_update(d, u):
        for k, v in u.items():
            if isinstance(v, Mapping):
                d[k] = deep_update(d.get(k, {}), v)
                d[k] = v
        return d

    total_samples = 0
    total_num_samples = spec.get("num_samples", 1)
    # For each preset, overwrite the spec and count the samples generated
    # for this preset
    for preset in presets:
        preset_spec = copy.deepcopy(spec)
        deep_update(preset_spec["config"], preset)
        total_samples += _count_spec_samples(preset_spec, 1)
        total_num_samples -= 1

    # Add the remaining samples
    if total_num_samples > 0:
        total_samples += _count_spec_samples(spec, total_num_samples)
    return total_samples

def _generate_variants_internal(
    spec: Dict, constant_grid_search: bool = False, random_state: "RandomState" = None
) -> Tuple[Dict, Dict]:
    spec = copy.deepcopy(spec)
    _, domain_vars, grid_vars = parse_spec_vars(spec)

    if not domain_vars and not grid_vars:
        yield {}, spec

    # Variables to resolve
    to_resolve = domain_vars

    all_resolved = True
    if constant_grid_search:
        # In this path, we first sample random variables and keep them constant
        # for grid search.
        # `_resolve_domain_vars` will alter `spec` directly
        all_resolved, resolved_vars = _resolve_domain_vars(
            spec, domain_vars, allow_fail=True, random_state=random_state
        if not all_resolved:
            # Not all variables have been resolved, but remove those that have
            # from the `to_resolve` list.
            to_resolve = [(r, d) for r, d in to_resolve if r not in resolved_vars]
    grid_search = _grid_search_generator(spec, grid_vars)
    for resolved_spec in grid_search:
        if not constant_grid_search or not all_resolved:
            # In this path, we sample the remaining random variables
            _, resolved_vars = _resolve_domain_vars(
                resolved_spec, to_resolve, random_state=random_state

        for resolved, spec in _generate_variants_internal(
            for path, value in grid_vars:
                resolved_vars[path] = _get_value(spec, path)
            for k, v in resolved.items():
                if (
                    k in resolved_vars
                    and v != resolved_vars[k]
                    and _is_resolved(resolved_vars[k])
                    raise ValueError(
                        "The variable `{}` could not be unambiguously "
                        "resolved to a single value. Consider simplifying "
                        "your configuration.".format(k)
                resolved_vars[k] = v
            yield resolved_vars, spec

def _get_preset_variants(
    spec: Dict,
    config: Dict,
    constant_grid_search: bool = False,
    random_state: "RandomState" = None,
    """Get variants according to a spec, initialized with a config.

    Variables from the spec are overwritten by the variables in the config.
    Thus, we may end up with less sampled parameters.

    This function also checks if values used to overwrite search space
    parameters are valid, and logs a warning if not.
    spec = copy.deepcopy(spec)

    resolved, _, _ = parse_spec_vars(config)

    for path, val in resolved:
            domain = _get_value(spec["config"], path)
            if isinstance(domain, dict):
                if "grid_search" in domain:
                    domain = Categorical(domain["grid_search"])
                    # If users want to overwrite an entire subdict,
                    # let them do it.
                    domain = None
        except IndexError as exc:
            raise ValueError(
                f"Pre-set config key `{'/'.join(path)}` does not correspond "
                f"to a valid key in the search space definition. Please add "
                f"this path to the `param_space` variable passed to `tune.Tuner()`."
            ) from exc

        if domain:
            if isinstance(domain, Domain):
                if not domain.is_valid(val):
                        f"Pre-set value `{val}` is not within valid values of "
                        f"parameter `{'/'.join(path)}`: {domain.domain_str}"
                # domain is actually a fixed value
                if domain != val:
                        f"Pre-set value `{val}` is not equal to the value of "
                        f"parameter `{'/'.join(path)}`: {domain}"
        assign_value(spec["config"], path, val)

    return _generate_variants_internal(
        spec, constant_grid_search=constant_grid_search, random_state=random_state

def assign_value(spec: Dict, path: Tuple, value: Any):
    for k in path[:-1]:
        spec = spec[k]
    spec[path[-1]] = value

def _get_value(spec: Dict, path: Tuple) -> Any:
    for k in path:
        spec = spec[k]
    return spec

def _resolve_domain_vars(
    spec: Dict,
    domain_vars: List[Tuple[Tuple, Domain]],
    allow_fail: bool = False,
    random_state: "RandomState" = None,
) -> Tuple[bool, Dict]:
    resolved = {}
    error = True
    num_passes = 0
    while error and num_passes < _MAX_RESOLUTION_PASSES:
        num_passes += 1
        error = False
        for path, domain in domain_vars:
            if path in resolved:
                value = domain.sample(
                    _UnresolvedAccessGuard(spec), random_state=random_state
            except RecursiveDependencyError as e:
                error = e
            except Exception:
                raise ValueError(
                    "Failed to evaluate expression: {}: {}".format(path, domain)
                assign_value(spec, path, value)
                resolved[path] = value
    if error:
        if not allow_fail:
            raise error
            return False, resolved
    return True, resolved

def _grid_search_generator(
    unresolved_spec: Dict, grid_vars: List
) -> Generator[Dict, None, None]:
    value_indices = [0] * len(grid_vars)

    def increment(i):
        value_indices[i] += 1
        if value_indices[i] >= len(grid_vars[i][1]):
            value_indices[i] = 0
            if i + 1 < len(value_indices):
                return increment(i + 1)
                return True
        return False

    if not grid_vars:
        yield unresolved_spec

    while value_indices[-1] < len(grid_vars[-1][1]):
        spec = copy.deepcopy(unresolved_spec)
        for i, (path, values) in enumerate(grid_vars):
            assign_value(spec, path, values[value_indices[i]])
        yield spec
        if grid_vars:
            done = increment(0)
            if done:

def _is_resolved(v) -> bool:
    resolved, _ = _try_resolve(v)
    return resolved

def _try_resolve(v) -> Tuple[bool, Any]:
    if isinstance(v, Domain):
        # Domain to sample from
        return False, v
    elif isinstance(v, dict) and len(v) == 1 and "eval" in v:
        # Lambda function in eval syntax
        return False, Function(
            lambda spec: eval(v["eval"], _STANDARD_IMPORTS, {"spec": spec})
    elif isinstance(v, dict) and len(v) == 1 and "grid_search" in v:
        # Grid search values
        grid_values = v["grid_search"]
        return False, Categorical(grid_values).grid()
    return True, v

def _split_resolved_unresolved_values(
    spec: Dict,
) -> Tuple[Dict[Tuple, Any], Dict[Tuple, Any]]:
    resolved_vars = {}
    unresolved_vars = {}
    for k, v in spec.items():
        resolved, v = _try_resolve(v)
        if not resolved:
            unresolved_vars[(k,)] = v
        elif isinstance(v, dict):
            # Recurse into a dict
            ) = _split_resolved_unresolved_values(v)
            for (path, value) in _resolved_children.items():
                resolved_vars[(k,) + path] = value
            for (path, value) in _unresolved_children.items():
                unresolved_vars[(k,) + path] = value
        elif isinstance(v, (list, tuple)):
            # Recurse into a list
            for i, elem in enumerate(v):
                ) = _split_resolved_unresolved_values({i: elem})
                for (path, value) in _resolved_children.items():
                    resolved_vars[(k,) + path] = value
                for (path, value) in _unresolved_children.items():
                    unresolved_vars[(k,) + path] = value
            resolved_vars[(k,)] = v
    return resolved_vars, unresolved_vars

def _unresolved_values(spec: Dict) -> Dict[Tuple, Any]:
    return _split_resolved_unresolved_values(spec)[1]

def _has_unresolved_values(spec: Dict) -> bool:
    return True if _unresolved_values(spec) else False

class _UnresolvedAccessGuard(dict):
    def __init__(self, *args, **kwds):
        super(_UnresolvedAccessGuard, self).__init__(*args, **kwds)
        self.__dict__ = self

    def __getattribute__(self, item):
        value = dict.__getattribute__(self, item)
        if not _is_resolved(value):
            raise RecursiveDependencyError(
                "`{}` recursively depends on {}".format(item, value)
        elif isinstance(value, dict):
            return _UnresolvedAccessGuard(value)
            return value

class RecursiveDependencyError(Exception):
    def __init__(self, msg: str):
        Exception.__init__(self, msg)