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"""

| This file contains SDC utility functions related to typing compilation phase

"""

import numpy
import numba
import sdc

from numba import types
from numba.core.errors import TypingError
from numba.np import numpy_support

from sdc.datatypes.indexes import *
from sdc.str_arr_type import string_array_type, StringArrayType
from sdc.datatypes.categorical.types import Categorical


sdc_old_index_types = (types.Array, StringArrayType, )
sdc_pandas_index_types = (
        EmptyIndexType,
        PositionalIndexType,
        RangeIndexType,
        Int64IndexType,
        MultiIndexType,
    ) + sdc_old_index_types

sdc_indexes_range_like = (
        PositionalIndexType,
        RangeIndexType,
    )

# TO-DO: support caching of data allocated for range indexes at request for .values
sdc_indexes_wo_values_cache = (
        EmptyIndexType,
        PositionalIndexType,
        RangeIndexType,
    )

sdc_pandas_df_column_types = (
        types.Array,
        StringArrayType,
        Categorical,
    )


class TypeChecker:
    """
        Validate object type and raise TypingError if the type is invalid, e.g.:
            Method nsmallest(). The object n
             given: bool
             expected: int
    """
    msg_template = '{} The object {}\n given: {}\n expected: {}'

    def __init__(self, func_name):
        """
        Parameters
        ----------
        func_name: :obj:`str`
            name of the function where types checking
        """
        self.func_name = func_name

    def raise_exc(self, data, expected_types, name=''):
        """
        Raise exception with unified message
        Parameters
        ----------
        data: :obj:`any`
            real type of the data
        expected_types: :obj:`str`
            expected types inserting directly to the exception
        name: :obj:`str`
            name of the parameter
        """
        msg = self.msg_template.format(self.func_name, name, data, expected_types)
        raise TypingError(msg)

    def check(self, data, accepted_type, name=''):
        """
        Check data type belongs to specified type
        Parameters
        ----------
        data: :obj:`any`
            real type of the data
        accepted_type: :obj:`type`
            accepted type
        name: :obj:`str`
            name of the parameter
        """
        if not isinstance(data, accepted_type):
            self.raise_exc(data, accepted_type.__name__, name=name)


class SDCLimitation(Exception):
    """Exception to be raised in case of SDC limitation"""
    pass


def kwsparams2list(params):
    """Convert parameters dict to a list of string of a format 'key=value'"""
    return ['{}={}'.format(k, v) for k, v in params.items()]


def sigparams2list(param_names, defaults):
    """Creates a list of strings of a format 'key=value' from parameter names and default values"""
    return [(f'{param}' if param not in defaults else f'{param}={defaults[param]}') for param in param_names]


def has_literal_value(var, value):
    """Used during typing to check that variable var is a Numba literal value equal to value"""

    if not isinstance(var, types.Literal):
        return False

    if value is None:
        return isinstance(var, types.NoneType) or var.literal_value is value
    elif isinstance(value, type(bool)):
        return var.literal_value is value
    else:
        return var.literal_value == value


def has_python_value(var, value):
    """Used during typing to check that variable var was resolved as Python type and has specific value"""

    if not isinstance(var, type(value)):
        return False

    if value is None or isinstance(value, type(bool)):
        return var is value
    else:
        return var == value


def is_default(var, value):
    return has_literal_value(var, value) or has_python_value(var, value) or isinstance(var, types.Omitted)


def check_is_numeric_array(type_var):
    """Used during typing to check that type_var is a numeric numpy arrays"""
    return check_is_array_of_dtype(type_var, types.Number)


def check_index_is_numeric(ty_series):
    """Used during typing to check that series has numeric index"""
    return isinstance(ty_series.index.dtype, types.Number)


def check_types_comparable(ty_left, ty_right):
    """Used during typing to check that specified types can be compared"""

    if hasattr(ty_left, 'dtype'):
        ty_left = ty_left.dtype

    if hasattr(ty_right, 'dtype'):
        ty_right = ty_right.dtype

    # add the rest of supported types here
    if isinstance(ty_left, types.Number):
        return isinstance(ty_right, types.Number)
    if isinstance(ty_left, types.UnicodeType):
        return isinstance(ty_right, types.UnicodeType)
    if isinstance(ty_left, types.Boolean):
        return isinstance(ty_right, types.Boolean)
    if isinstance(ty_left, (types.Tuple, types.UniTuple)):
        # FIXME: just for now to unblock compilation
        return ty_left == ty_right

    return False


def check_arrays_comparable(ty_left, ty_right):
    """Used during typing to check that underlying arrays of specified types can be compared"""
    return ((ty_left == string_array_type and ty_right == string_array_type)
            or (check_is_numeric_array(ty_left) and check_is_numeric_array(ty_right)))


def check_is_array_of_dtype(type_var, dtype):
    """Used during typing to check that type_var is a numeric numpy array of specific dtype"""
    return isinstance(type_var, types.Array) and isinstance(type_var.dtype, dtype)


def find_common_dtype_from_numpy_dtypes(array_types, scalar_types):
    """Used to find common numba dtype for a sequences of numba dtypes each representing some numpy dtype"""
    np_array_dtypes = [numpy_support.as_dtype(dtype) for dtype in array_types]
    np_scalar_dtypes = [numpy_support.as_dtype(dtype) for dtype in scalar_types]
    np_common_dtype = numpy.find_common_type(np_array_dtypes, np_scalar_dtypes)
    numba_common_dtype = numpy_support.from_dtype(np_common_dtype)

    return numba_common_dtype


def find_index_common_dtype(left, right):
    """Used to find common dtype for indexes of two series and verify if index dtypes are equal"""

    left_index_dtype = left.dtype
    right_index_dtype = right.dtype
    index_dtypes_match = left_index_dtype == right_index_dtype
    if not index_dtypes_match:
        numba_index_common_dtype = find_common_dtype_from_numpy_dtypes(
            [left_index_dtype, right_index_dtype], [])
    else:
        numba_index_common_dtype = left_index_dtype

    return index_dtypes_match, numba_index_common_dtype


def gen_impl_generator(codegen, impl_name):
    """Generate generator of an implementation"""
    def _df_impl_generator(*args, **kwargs):
        func_text, global_vars = codegen(*args, **kwargs)

        loc_vars = {}
        exec(func_text, global_vars, loc_vars)
        _impl = loc_vars[impl_name]

        return _impl

    return _df_impl_generator


def check_signed_integer(ty):
    return isinstance(ty, types.Integer) and ty.signed


def _check_dtype_param_type(dtype):
    """ Returns True is dtype is a valid type for dtype parameter and False otherwise.
        Used in RangeIndex ctor and other methods that take dtype parameter. """

    valid_dtype_types = (types.NoneType, types.Omitted, types.UnicodeType, types.NumberClass)
    return isinstance(dtype, valid_dtype_types) or dtype is None

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