from __future__ import annotations

from copy import deepcopy
from collections import defaultdict

from sqlglot import exp, transforms
from sqlglot.dialects.dialect import (
    date_delta_sql,
    timestamptrunc_sql,
    groupconcat_sql,
)
from sqlglot.dialects.spark import Spark
from sqlglot.parsers.databricks import DatabricksParser
from sqlglot.tokens import TokenType
from sqlglot.optimizer.annotate_types import TypeAnnotator


def _jsonextract_sql(
    self: Databricks.Generator, expression: exp.JSONExtract | exp.JSONExtractScalar
) -> str:
    this = self.sql(expression, "this")
    expr = self.sql(expression, "expression")
    return f"{this}:{expr}"


class Databricks(Spark):
    SAFE_DIVISION = False
    COPY_PARAMS_ARE_CSV = False

    COERCES_TO = defaultdict(set, deepcopy(TypeAnnotator.COERCES_TO))
    for text_type in exp.DataType.TEXT_TYPES:
        COERCES_TO[text_type] |= {
            *exp.DataType.NUMERIC_TYPES,
            *exp.DataType.TEMPORAL_TYPES,
            exp.DType.BINARY,
            exp.DType.BOOLEAN,
            exp.DType.INTERVAL,
        }

    class JSONPathTokenizer(Spark.JSONPathTokenizer):
        IDENTIFIERS = ["`", '"']

    class Tokenizer(Spark.Tokenizer):
        KEYWORDS = {
            **Spark.Tokenizer.KEYWORDS,
            "STREAM": TokenType.STREAM,
            "VOID": TokenType.VOID,
        }

    Parser = DatabricksParser

    class Generator(Spark.Generator):
        TABLESAMPLE_SEED_KEYWORD = "REPEATABLE"
        COPY_PARAMS_ARE_WRAPPED = False
        COPY_PARAMS_EQ_REQUIRED = True
        JSON_PATH_SINGLE_QUOTE_ESCAPE = False
        SAFE_JSON_PATH_KEY_RE = exp.SAFE_IDENTIFIER_RE
        QUOTE_JSON_PATH = False
        PARSE_JSON_NAME = "PARSE_JSON"

        TRANSFORMS = {
            **Spark.Generator.TRANSFORMS,
            exp.CurrentVersion: lambda *_: "CURRENT_VERSION()",
            exp.DateAdd: date_delta_sql("DATEADD"),
            exp.DateDiff: date_delta_sql("DATEDIFF"),
            exp.DatetimeAdd: lambda self, e: self.func(
                "TIMESTAMPADD", e.unit, e.expression, e.this
            ),
            exp.DatetimeSub: lambda self, e: self.func(
                "TIMESTAMPADD",
                e.unit,
                exp.Mul(this=e.expression, expression=exp.Literal.number(-1)),
                e.this,
            ),
            exp.DatetimeTrunc: timestamptrunc_sql(),
            exp.GroupConcat: groupconcat_sql,
            exp.Select: transforms.preprocess(
                [
                    transforms.eliminate_distinct_on,
                    transforms.unnest_to_explode,
                    transforms.any_to_exists,
                ]
            ),
            exp.JSONExtract: _jsonextract_sql,
            exp.JSONExtractScalar: _jsonextract_sql,
            exp.JSONPathRoot: lambda *_: "",
            exp.ToChar: lambda self, e: (
                self.cast_sql(exp.Cast(this=e.this, to=exp.DataType(this="STRING")))
                if e.args.get("is_numeric")
                else self.function_fallback_sql(e)
            ),
            exp.CurrentCatalog: lambda *_: "CURRENT_CATALOG()",
        }

        TRANSFORMS.pop(exp.RegexpLike)
        TRANSFORMS.pop(exp.TryCast)

        TYPE_MAPPING = {
            **Spark.Generator.TYPE_MAPPING,
            exp.DType.NULL: "VOID",
        }

        def columndef_sql(self, expression: exp.ColumnDef, sep: str = " ") -> str:
            constraint = expression.find(exp.GeneratedAsIdentityColumnConstraint)
            kind = expression.kind
            if (
                constraint
                and isinstance(kind, exp.DataType)
                and kind.this in exp.DataType.INTEGER_TYPES
            ):
                # only BIGINT generated identity constraints are supported
                expression.set("kind", exp.DataType.build("bigint"))

            return super().columndef_sql(expression, sep)

        def jsonpath_sql(self, expression: exp.JSONPath) -> str:
            expression.set("escape", None)
            return super().jsonpath_sql(expression)

        def uniform_sql(self, expression: exp.Uniform) -> str:
            gen = expression.args.get("gen")
            seed = expression.args.get("seed")

            # From Snowflake UNIFORM(min, max, gen) as RANDOM(), RANDOM(seed), or constant value -> Extract seed
            if gen:
                seed = gen.this

            return self.func("UNIFORM", expression.this, expression.expression, seed)
