"""This module contains the _EstimatorPrettyPrinter class used in
BaseEstimator.__repr__ for pretty-printing estimators"""

# Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
# 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 Python Software Foundation;
# All Rights Reserved

# Authors: Fred L. Drake, Jr. <fdrake@acm.org> (built-in CPython pprint module)
#          Nicolas Hug (scikit-learn specific changes)

# License: PSF License version 2 (see below)

# PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
# --------------------------------------------

# 1. This LICENSE AGREEMENT is between the Python Software Foundation ("PSF"),
# and the Individual or Organization ("Licensee") accessing and otherwise
# using this software ("Python") in source or binary form and its associated
# documentation.

# 2. Subject to the terms and conditions of this License Agreement, PSF hereby
# grants Licensee a nonexclusive, royalty-free, world-wide license to
# reproduce, analyze, test, perform and/or display publicly, prepare
# derivative works, distribute, and otherwise use Python alone or in any
# derivative version, provided, however, that PSF's License Agreement and
# PSF's notice of copyright, i.e., "Copyright (c) 2001, 2002, 2003, 2004,
# 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,
# 2017, 2018 Python Software Foundation; All Rights Reserved" are retained in
# Python alone or in any derivative version prepared by Licensee.

# 3. In the event Licensee prepares a derivative work that is based on or
# incorporates Python or any part thereof, and wants to make the derivative
# work available to others as provided herein, then Licensee hereby agrees to
# include in any such work a brief summary of the changes made to Python.

# 4. PSF is making Python available to Licensee on an "AS IS" basis. PSF MAKES
# NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT
# NOT LIMITATION, PSF MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY OF
# MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF
# PYTHON WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.

# 5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON FOR ANY
# INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF
# MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON, OR ANY DERIVATIVE
# THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.

# 6. This License Agreement will automatically terminate upon a material
# breach of its terms and conditions.

# 7. Nothing in this License Agreement shall be deemed to create any
# relationship of agency, partnership, or joint venture between PSF and
# Licensee. This License Agreement does not grant permission to use PSF
# trademarks or trade name in a trademark sense to endorse or promote products
# or services of Licensee, or any third party.

# 8. By copying, installing or otherwise using Python, Licensee agrees to be
# bound by the terms and conditions of this License Agreement.


# Brief summary of changes to original code:
# - "compact" parameter is supported for dicts, not just lists or tuples
# - estimators have a custom handler, they're not just treated as objects
# - long sequences (lists, tuples, dict items) with more than N elements are
#   shortened using ellipsis (', ...') at the end.

from inspect import signature
import pprint
from collections import OrderedDict

from ..base import BaseEstimator
from .._config import get_config
from . import is_scalar_nan


class KeyValTuple(tuple):
    """Dummy class for correctly rendering key-value tuples from dicts."""
    def __repr__(self):
        # needed for _dispatch[tuple.__repr__] not to be overridden
        return super().__repr__()


class KeyValTupleParam(KeyValTuple):
    """Dummy class for correctly rendering key-value tuples from parameters."""
    pass


def _changed_params(estimator):
    """Return dict (param_name: value) of parameters that were given to
    estimator with non-default values."""

    params = estimator.get_params(deep=False)
    filtered_params = {}
    init_func = getattr(estimator.__init__, 'deprecated_original',
                        estimator.__init__)
    init_params = signature(init_func).parameters
    init_params = {name: param.default for name, param in init_params.items()}

    for k, v in params.items():
        if (k not in init_params or (  # happens if k is part of a **kwargs
                repr(v) != repr(init_params[k]) and
                not (is_scalar_nan(init_params[k]) and is_scalar_nan(v)))):
            filtered_params[k] = v
    return filtered_params


class _EstimatorPrettyPrinter(pprint.PrettyPrinter):
    """Pretty Printer class for estimator objects.

    This extends the pprint.PrettyPrinter class, because:
    - we need estimators to be printed with their parameters, e.g.
      Estimator(param1=value1, ...) which is not supported by default.
    - the 'compact' parameter of PrettyPrinter is ignored for dicts, which
      may lead to very long representations that we want to avoid.

    Quick overview of pprint.PrettyPrinter (see also
    https://stackoverflow.com/questions/49565047/pprint-with-hex-numbers):

    - the entry point is the _format() method which calls format() (overridden
      here)
    - format() directly calls _safe_repr() for a first try at rendering the
      object
    - _safe_repr formats the whole object reccursively, only calling itself,
      not caring about line length or anything
    - back to _format(), if the output string is too long, _format() then calls
      the appropriate _pprint_TYPE() method (e.g. _pprint_list()) depending on
      the type of the object. This where the line length and the compact
      parameters are taken into account.
    - those _pprint_TYPE() methods will internally use the format() method for
      rendering the nested objects of an object (e.g. the elements of a list)

    In the end, everything has to be implemented twice: in _safe_repr and in
    the custom _pprint_TYPE methods. Unfortunately PrettyPrinter is really not
    straightforward to extend (especially when we want a compact output), so
    the code is a bit convoluted.

    This class overrides:
    - format() to support the changed_only parameter
    - _safe_repr to support printing of estimators (for when they fit on a
      single line)
    - _format_dict_items so that dict are correctly 'compacted'
    - _format_items so that ellipsis is used on long lists and tuples

    When estimators cannot be printed on a single line, the builtin _format()
    will call _pprint_estimator() because it was registered to do so (see
    _dispatch[BaseEstimator.__repr__] = _pprint_estimator).

    both _format_dict_items() and _pprint_estimator() use the
    _format_params_or_dict_items() method that will format parameters and
    key-value pairs respecting the compact parameter. This method needs another
    subroutine _pprint_key_val_tuple() used when a parameter or a key-value
    pair is too long to fit on a single line. This subroutine is called in
    _format() and is registered as well in the _dispatch dict (just like
    _pprint_estimator). We had to create the two classes KeyValTuple and
    KeyValTupleParam for this.
    """

    def __init__(self, indent=1, width=80, depth=None, stream=None, *,
                 compact=False, indent_at_name=True,
                 n_max_elements_to_show=None):
        super().__init__(indent, width, depth, stream, compact=compact)
        self._indent_at_name = indent_at_name
        if self._indent_at_name:
            self._indent_per_level = 1  # ignore indent param
        self._changed_only = get_config()['print_changed_only']
        # Max number of elements in a list, dict, tuple until we start using
        # ellipsis. This also affects the number of arguments of an estimators
        # (they are treated as dicts)
        self.n_max_elements_to_show = n_max_elements_to_show

    def format(self, object, context, maxlevels, level):
        return _safe_repr(object, context, maxlevels, level,
                          changed_only=self._changed_only)

    def _pprint_estimator(self, object, stream, indent, allowance, context,
                          level):
        stream.write(object.__class__.__name__ + '(')
        if self._indent_at_name:
            indent += len(object.__class__.__name__)

        if self._changed_only:
            params = _changed_params(object)
        else:
            params = object.get_params(deep=False)

        params = OrderedDict((name, val)
                             for (name, val) in sorted(params.items()))

        self._format_params(params.items(), stream, indent, allowance + 1,
                            context, level)
        stream.write(')')

    def _format_dict_items(self, items, stream, indent, allowance, context,
                           level):
        return self._format_params_or_dict_items(
            items, stream, indent, allowance, context, level, is_dict=True)

    def _format_params(self, items, stream, indent, allowance, context, level):
        return self._format_params_or_dict_items(
            items, stream, indent, allowance, context, level, is_dict=False)

    def _format_params_or_dict_items(self, object, stream, indent, allowance,
                                     context, level, is_dict):
        """Format dict items or parameters respecting the compact=True
        parameter. For some reason, the builtin rendering of dict items doesn't
        respect compact=True and will use one line per key-value if all cannot
        fit in a single line.
        Dict items will be rendered as <'key': value> while params will be
        rendered as <key=value>. The implementation is mostly copy/pasting from
        the builtin _format_items().
        This also adds ellipsis if the number of items is greater than
        self.n_max_elements_to_show.
        """
        write = stream.write
        indent += self._indent_per_level
        delimnl = ',\n' + ' ' * indent
        delim = ''
        width = max_width = self._width - indent + 1
        it = iter(object)
        try:
            next_ent = next(it)
        except StopIteration:
            return
        last = False
        n_items = 0
        while not last:
            if n_items == self.n_max_elements_to_show:
                write(', ...')
                break
            n_items += 1
            ent = next_ent
            try:
                next_ent = next(it)
            except StopIteration:
                last = True
                max_width -= allowance
                width -= allowance
            if self._compact:
                k, v = ent
                krepr = self._repr(k, context, level)
                vrepr = self._repr(v, context, level)
                if not is_dict:
                    krepr = krepr.strip("'")
                middle = ': ' if is_dict else '='
                rep = krepr + middle + vrepr
                w = len(rep) + 2
                if width < w:
                    width = max_width
                    if delim:
                        delim = delimnl
                if width >= w:
                    width -= w
                    write(delim)
                    delim = ', '
                    write(rep)
                    continue
            write(delim)
            delim = delimnl
            class_ = KeyValTuple if is_dict else KeyValTupleParam
            self._format(class_(ent), stream, indent,
                         allowance if last else 1, context, level)

    def _format_items(self, items, stream, indent, allowance, context, level):
        """Format the items of an iterable (list, tuple...). Same as the
        built-in _format_items, with support for ellipsis if the number of
        elements is greater than self.n_max_elements_to_show.
        """
        write = stream.write
        indent += self._indent_per_level
        if self._indent_per_level > 1:
            write((self._indent_per_level - 1) * ' ')
        delimnl = ',\n' + ' ' * indent
        delim = ''
        width = max_width = self._width - indent + 1
        it = iter(items)
        try:
            next_ent = next(it)
        except StopIteration:
            return
        last = False
        n_items = 0
        while not last:
            if n_items == self.n_max_elements_to_show:
                write(', ...')
                break
            n_items += 1
            ent = next_ent
            try:
                next_ent = next(it)
            except StopIteration:
                last = True
                max_width -= allowance
                width -= allowance
            if self._compact:
                rep = self._repr(ent, context, level)
                w = len(rep) + 2
                if width < w:
                    width = max_width
                    if delim:
                        delim = delimnl
                if width >= w:
                    width -= w
                    write(delim)
                    delim = ', '
                    write(rep)
                    continue
            write(delim)
            delim = delimnl
            self._format(ent, stream, indent,
                         allowance if last else 1, context, level)

    def _pprint_key_val_tuple(self, object, stream, indent, allowance, context,
                              level):
        """Pretty printing for key-value tuples from dict or parameters."""
        k, v = object
        rep = self._repr(k, context, level)
        if isinstance(object, KeyValTupleParam):
            rep = rep.strip("'")
            middle = '='
        else:
            middle = ': '
        stream.write(rep)
        stream.write(middle)
        self._format(v, stream, indent + len(rep) + len(middle), allowance,
                     context, level)

    # Note: need to copy _dispatch to prevent instances of the builtin
    # PrettyPrinter class to call methods of _EstimatorPrettyPrinter (see issue
    # 12906)
    # mypy error: "Type[PrettyPrinter]" has no attribute "_dispatch"
    _dispatch = pprint.PrettyPrinter._dispatch.copy()  # type: ignore
    _dispatch[BaseEstimator.__repr__] = _pprint_estimator
    _dispatch[KeyValTuple.__repr__] = _pprint_key_val_tuple


def _safe_repr(object, context, maxlevels, level, changed_only=False):
    """Same as the builtin _safe_repr, with added support for Estimator
    objects."""
    typ = type(object)

    if typ in pprint._builtin_scalars:
        return repr(object), True, False

    r = getattr(typ, "__repr__", None)
    if issubclass(typ, dict) and r is dict.__repr__:
        if not object: