





import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


_UpperCAmelCase				:       Any	   =				logging.get_logger(__name__)


_UpperCAmelCase				:       int	   =				R"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""

class    lowerCAmelCase							(					__UpperCamelCase			):
 @add_start_docstrings(UpperCAmelCase	)
 def __call__(      self  :      Optional[Any]			,     UpperCAmelCase  :      torch.LongTensor			,     UpperCAmelCase  :      torch.FloatTensor			,     **UpperCAmelCase  :      List[Any]	)			->	bool:
     raise NotImplementedError('StoppingCriteria needs to be subclassed'	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Optional[int] = None	)			->	Optional[Any]:
     lowerCamelCase__    :      List[str]					 =		max_length
     lowerCamelCase__    :      Optional[Any]					 =		max_position_embeddings







 @add_start_docstrings(UpperCAmelCase	)
 def __call__(      self  :      Optional[Any]			,     UpperCAmelCase  :      torch.LongTensor			,     UpperCAmelCase  :      torch.FloatTensor			,     **UpperCAmelCase  :      Optional[Any]	)			->	bool:
     lowerCamelCase__    :      Optional[int]					 =		input_ids.shape[-1]
     lowerCamelCase__    :      int					 =		cur_len >= self.max_length
     if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
         logger.warning_once(
             'This is a friendly reminder - the current text generation call will exceed the model\'s predefined '
             F"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
             'exceptions, performance degradation, or nothing at all.'	)
     return is_done


class    lowerCAmelCase							(					__UpperCamelCase			):
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      int	)			->	Dict:
     warnings.warn(
         'The class `MaxNewTokensCriteria` is deprecated. '
         F"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
         'with `max_length = start_length + max_new_tokens` instead.'			,     UpperCAmelCase			,     )
     lowerCamelCase__    :      Dict					 =		start_length
     lowerCamelCase__    :      str					 =		max_new_tokens
     lowerCamelCase__    :      Optional[Any]					 =		start_length + max_new_tokens







 @add_start_docstrings(UpperCAmelCase	)
 def __call__(      self  :      List[Any]			,     UpperCAmelCase  :      torch.LongTensor			,     UpperCAmelCase  :      torch.FloatTensor			,     **UpperCAmelCase  :      Optional[Any]	)			->	bool:
     return input_ids.shape[-1] >= self.max_length


class    lowerCAmelCase							(					__UpperCamelCase			):
 def __init__(      self  :      Union[str, Any]			,     UpperCAmelCase  :      float			,     UpperCAmelCase  :      Optional[float] = None	)			->	List[str]:
     lowerCamelCase__    :      Union[str, Any]					 =		max_time
     lowerCamelCase__    :      int					 =		time.time() if initial_timestamp is None else initial_timestamp







 @add_start_docstrings(UpperCAmelCase	)
 def __call__(      self  :      int			,     UpperCAmelCase  :      torch.LongTensor			,     UpperCAmelCase  :      torch.FloatTensor			,     **UpperCAmelCase  :      str	)			->	bool:
     return time.time() - self.initial_timestamp > self.max_time


class    lowerCAmelCase							(					__UpperCamelCase			):
 @add_start_docstrings(UpperCAmelCase	)
 def __call__(      self  :      Dict			,     UpperCAmelCase  :      torch.LongTensor			,     UpperCAmelCase  :      torch.FloatTensor			,     **UpperCAmelCase  :      int	)			->	bool:
     return any(criteria(UpperCAmelCase			,     UpperCAmelCase	) for criteria in self	)







 @property
 def      A_	(      self  :      str	)			->	Optional[int]:
     for stopping_criterium in self:
         if isinstance(UpperCAmelCase			,     UpperCAmelCase	):
             return stopping_criterium.max_length
         elif isinstance(UpperCAmelCase			,     UpperCAmelCase	):
             return stopping_criterium.max_length
     return None


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     StoppingCriteriaList:
    lowerCamelCase__    :      Union[str, Any]					 =		stopping_criteria.max_length
    lowerCamelCase__    :      List[str]					 =		deepcopy(_UpperCAmelCase )
    if stopping_max_length is not None and stopping_max_length != max_length:
        warnings.warn('You set different `max_length` for stopping criteria and `max_length` parameter'						,	_UpperCAmelCase )
    elif stopping_max_length is None:
        new_stopping_criteria.append(MaxLengthCriteria(max_length=_UpperCAmelCase ) )
    return new_stopping_criteria












from __future__ import annotations


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     list[tuple[int, int]]:
    lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		position
    lowerCamelCase__    :      Optional[Any]					 =		[
        (y + 1, x + 2),
        (y - 1, x + 2),
        (y + 1, x - 2),
        (y - 1, x - 2),
        (y + 2, x + 1),
        (y + 2, x - 1),
        (y - 2, x + 1),
        (y - 2, x - 1),
    ]
    lowerCamelCase__    :      Dict					 =		[]

    for position in positions:
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position
        if 0 <= y_test < n and 0 <= x_test < n:
            permissible_positions.append(_UpperCAmelCase )

    return permissible_positions


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     bool:
    return not any(elem == 0 for row in board for elem in row )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )			->     bool:

    if is_complete(_UpperCAmelCase ):
        return True

    for position in get_valid_pos(_UpperCAmelCase						,	len(_UpperCAmelCase ) ):
        lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		position

        if board[y][x] == 0:
            lowerCamelCase__    :      List[Any]					 =		curr + 1
            if open_knight_tour_helper(_UpperCAmelCase						,	_UpperCAmelCase						,	curr + 1 ):
                return True
            lowerCamelCase__    :      Optional[Any]					 =		0

    return False


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     list[list[int]]:
    lowerCamelCase__    :      Any					 =		[[0 for i in range(_UpperCAmelCase )] for j in range(_UpperCAmelCase )]

    for i in range(_UpperCAmelCase ):
        for j in range(_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[int]					 =		1
            if open_knight_tour_helper(_UpperCAmelCase						,	(i, j)						,	1 ):
                return board
            lowerCamelCase__    :      Optional[Any]					 =		0

    lowerCamelCase__    :      Any					 =		F"""Open Kight Tour cannot be performed on a board of size {n}"""
    raise ValueError(_UpperCAmelCase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





