import json
import re

#!!! This giant block of imports should be something simpler, such as:
# from great_exepectations.helpers.expectation_creation import *
from great_expectations.execution_engine import (
    PandasExecutionEngine,
    SparkDFExecutionEngine,
    SqlAlchemyExecutionEngine,
)
from great_expectations.expectations.expectation import (
    ColumnMapExpectation,
    Expectation,
    ExpectationConfiguration,
)
from great_expectations.expectations.metrics import (
    ColumnMapMetricProvider,
    column_condition_partial,
)
from great_expectations.expectations.registry import (
    _registered_expectations,
    _registered_metrics,
    _registered_renderers,
)
from great_expectations.expectations.util import render_evaluation_parameter_string
from great_expectations.render.renderer.renderer import renderer
from great_expectations.render.types import RenderedStringTemplateContent
from great_expectations.render.util import num_to_str, substitute_none_for_missing
from great_expectations.validator.validator import Validator


# This method compares a string to a url validation regex
def fits_regex(x):
    regex = re.compile(
        r"^(?:http|ftp)s?://"  # http:// or https://
        r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|"  # domain...
        r"localhost|"  # localhost...
        r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})"  # ...or ip
        r"(?::\d+)?"  # optional port
        r"(?:/?|[/?]\S+)$",
        re.IGNORECASE,
    )
    if re.match(regex, str(x)):
        return True
    return False


# This class defines a Metric to support your Expectation
# For most Expectations, the main business logic for calculation will live here.
# To learn about the relationship between Metrics and Expectations, please visit {some doc}.
class ColumnValuesToBeValidUrls(ColumnMapMetricProvider):

    # This is the id string that will be used to reference your metric.
    # Please see {some doc} for information on how to choose an id string for your Metric.
    condition_metric_name = "column_values.to_be_valid_urls"

    # This method defines the business logic for evaluating your metric when using a PandasExecutionEngine

    @column_condition_partial(engine=PandasExecutionEngine)
    def _pandas(cls, column, **kwargs):
        return column.apply(lambda x: fits_regex(x))


# This method defines the business logic for evaluating your metric when using a SqlAlchemyExecutionEngine
#     @column_condition_partial(engine=SqlAlchemyExecutionEngine)
#     def _sqlalchemy(cls, column, _dialect, **kwargs):
#         return column.in_([3])

# This method defines the business logic for evaluating your metric when using a SparkDFExecutionEngine
#     @column_condition_partial(engine=SparkDFExecutionEngine)
#     def _spark(cls, column, **kwargs):
#         return column.isin([3])


# This class defines the Expectation itself
# The main business logic for calculation lives here.
class ExpectColumnValuesToBeValidUrls(ColumnMapExpectation):
    """Expect the column to be a valid url.  Maps row values to regex to check if value is a valid url."""

    examples = [
        {
            "data": {
                "mostly_urls": [
                    "http://www.caseycaruso.com",
                    "http://www.bvp.com",
                    "http://www.tlccollective.space",
                    "kittens",
                    "wwww.googlecom",
                ],
                "valid_urls": [
                    "http://www.facebook.com",
                    "http://www.twitter.com",
                    "http://www.github.com",
                    "http://www.stackoverflow.com",
                    "http://www.google.com",
                ],
            },
            "tests": [
                {
                    "title": "mostly_valid_urls",
                    "exact_match_out": False,
                    "include_in_gallery": True,
                    "in": {"column": "mostly_urls", "mostly": 0.1},
                    "out": {
                        "success": True,
                        "unexpected_index_list": [3, 4],
                        "unexpected_list": ["kittens", "wwww.googlecom"],
                    },
                },
                {
                    "title": "valid_urls",
                    "exact_match_out": False,
                    "include_in_gallery": True,
                    "in": {"column": "valid_urls", "mostly": 1},
                    "out": {
                        "success": True,
                        "unexpected_index_list": [],
                        "unexpected_list": [],
                    },
                },
            ],
        }
    ]

    # This dictionary contains metadata for display in the public gallery
    library_metadata = {
        "maturity": "experimental",
        "tags": ["experimental", "hackathon"],
        "contributors": [  # Github handles for all contributors to this Expectation
            "@ckathleen"
        ],
        "package": "experimental_expectations",
    }

    # This is the id string of the Metric used by this Expectation.
    # For most Expectations, it will be the same as the `condition_metric_name` defined in your Metric class above.
    map_metric = "column_values.to_be_valid_urls"

    # This is a list of parameter names that can affect whether the Expectation evaluates to True or False
    # Please see {some doc} for more information about domain and success keys, and other arguments to Expectations
    success_keys = ("mostly",)

    # This dictionary contains default values for any parameters that should have default values
    default_kwarg_values = {}

    # This method defines a question Renderer
    # For more info on Renderers, see {some doc}
    #!!! This example renderer should render RenderedStringTemplateContent, not just a string


#     @classmethod
#     @renderer(renderer_type="renderer.question")
#     def _question_renderer(
#         cls, configuration, result=None, language=None, runtime_configuration=None
#     ):
#         column = configuration.kwargs.get("column")
#         mostly = configuration.kwargs.get("mostly")

#         return f'Do at least {mostly * 100}% of values in column "{column}" equal 3?'

# This method defines an answer Renderer
#!!! This example renderer should render RenderedStringTemplateContent, not just a string
#     @classmethod
#     @renderer(renderer_type="renderer.answer")
#     def _answer_renderer(
#         cls, configuration=None, result=None, language=None, runtime_configuration=None
#     ):
#         column = result.expectation_config.kwargs.get("column")
#         mostly = result.expectation_config.kwargs.get("mostly")
#         regex = result.expectation_config.kwargs.get("regex")
#         if result.success:
#             return f'At least {mostly * 100}% of values in column "{column}" equal 3.'
#         else:
#             return f'Less than {mostly * 100}% of values in column "{column}" equal 3.'

# This method defines a prescriptive Renderer
#     @classmethod
#     @renderer(renderer_type="renderer.prescriptive")
#     @render_evaluation_parameter_string
#     def _prescriptive_renderer(
#         cls,
#         configuration=None,
#         result=None,
#         language=None,
#         runtime_configuration=None,
#         **kwargs,
#     ):
#!!! This example renderer should be shorter
#         runtime_configuration = runtime_configuration or {}
#         include_column_name = runtime_configuration.get("include_column_name", True)
#         include_column_name = (
#             include_column_name if include_column_name is not None else True
#         )
#         styling = runtime_configuration.get("styling")
#         params = substitute_none_for_missing(
#             configuration.kwargs,
#             ["column", "regex", "mostly", "row_condition", "condition_parser"],
#         )

#         template_str = "values must be equal to 3"
#         if params["mostly"] is not None:
#             params["mostly_pct"] = num_to_str(
#                 params["mostly"] * 100, precision=15, no_scientific=True
#             )
#             # params["mostly_pct"] = "{:.14f}".format(params["mostly"]*100).rstrip("0").rstrip(".")
#             template_str += ", at least $mostly_pct % of the time."
#         else:
#             template_str += "."

#         if include_column_name:
#             template_str = "$column " + template_str

#         if params["row_condition"] is not None:
#             (
#                 conditional_template_str,
#                 conditional_params,
#             ) = parse_row_condition_string_pandas_engine(params["row_condition"])
#             template_str = conditional_template_str + ", then " + template_str
#             params.update(conditional_params)

#         return [
#             RenderedStringTemplateContent(
#                 **{
#                     "content_block_type": "string_template",
#                     "string_template": {
#                         "template": template_str,
#                         "params": params,
#                         "styling": styling,
#                     },
#                 }
#             )
#         ]

if __name__ == "__main__":
    diagnostics_report = ExpectColumnValuesToBeValidUrls().run_diagnostics()
    print(json.dumps(diagnostics_report, indent=2))
