
"""simple docstring"""


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_lowercase				 =					logging.getLogger(__name__)







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     str
						_lowerCamelCase:     List[str]
						_lowerCamelCase:     Optional[List[str]]







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     List[int]
						_lowerCamelCase:     List[int]
						_lowerCamelCase:     Optional[List[int]]   =     None
						_lowerCamelCase:     Optional[List[int]]   =     None







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     '''train'''
						_lowerCamelCase:     Any   =     '''dev'''
						_lowerCamelCase:     Optional[Any]   =     '''test'''







class 					lowerCAmelCase_					:



						'''simple docstring'''


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   Dict	,A_  :   Union[Split, str]   )  -> List[InputExample]:
							raise NotImplementedError


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   str   )  -> List[str]:
							raise NotImplementedError




						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   List[InputExample]	,A_  :   List[str]	,A_  :   int	,A_  :   PreTrainedTokenizer	,A_  :   Optional[int]=False	,A_  :   Any="[CLS]"	,A_  :   Optional[Any]=1	,A_  :   List[Any]="[SEP]"	,A_  :   List[str]=False	,A_  :   Union[str, Any]=False	,A_  :   List[Any]=0	,A_  :   Optional[Any]=0	,A_  :   str=-100	,A_  :   Union[str, Any]=0	,A_  :   int=True	,)  -> List[InputFeatures]:
							A     = {label: i for i, label in enumerate(A_   )}

							A     = []
							for ex_index, example in enumerate(A_   ):
								if ex_index % 1_0000 == 0:
									logger.info('Writing example %d of %d'	,A_	,len(A_   )   )

								A     = []
								A     = []
								for word, label in zip(example.words	,example.labels   ):
									A     = tokenizer.tokenize(A_   )

									# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
									if len(A_   ) > 0:
										tokens.extend(A_   )
										# Use the real label id for the first token of the word, and padding ids for the remaining tokens
										label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(A_   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
								A     = tokenizer.num_special_tokens_to_add()
								if len(A_   ) > max_seq_length - special_tokens_count:
									A     = tokens[: (max_seq_length - special_tokens_count)]
									A     = label_ids[: (max_seq_length - special_tokens_count)]

								# The convention in BERT is:
								# (a) For sequence pairs:
								#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
								#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
								# (b) For single sequences:
								#  tokens:   [CLS] the dog is hairy . [SEP]
								#  type_ids:   0   0   0   0  0     0   0
								#
								# Where "type_ids" are used to indicate whether this is the first
								# sequence or the second sequence. The embedding vectors for `type=0` and
								# `type=1` were learned during pre-training and are added to the wordpiece
								# embedding vector (and position vector). This is not *strictly* necessary
								# since the [SEP] token unambiguously separates the sequences, but it makes
								# it easier for the model to learn the concept of sequences.
								#
								# For classification tasks, the first vector (corresponding to [CLS]) is
								# used as the "sentence vector". Note that this only makes sense because
								# the entire model is fine-tuned.
								tokens += [sep_token]
								label_ids += [pad_token_label_id]
								if sep_token_extra:
									# roberta uses an extra separator b/w pairs of sentences
									tokens += [sep_token]
									label_ids += [pad_token_label_id]
								A     = [sequence_a_segment_id] * len(A_   )

								if cls_token_at_end:
									tokens += [cls_token]
									label_ids += [pad_token_label_id]
									segment_ids += [cls_token_segment_id]
								else:
									A     = [cls_token] + tokens
									A     = [pad_token_label_id] + label_ids
									A     = [cls_token_segment_id] + segment_ids

								A     = tokenizer.convert_tokens_to_ids(A_   )

								# The mask has 1 for real tokens and 0 for padding tokens. Only real
								# tokens are attended to.
								A     = [1 if mask_padding_with_zero else 0] * len(A_   )

								# Zero-pad up to the sequence length.
								A     = max_seq_length - len(A_   )
								if pad_on_left:
									A     = ([pad_token] * padding_length) + input_ids
									A     = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
									A     = ([pad_token_segment_id] * padding_length) + segment_ids
									A     = ([pad_token_label_id] * padding_length) + label_ids
								else:
									input_ids += [pad_token] * padding_length
									input_mask += [0 if mask_padding_with_zero else 1] * padding_length
									segment_ids += [pad_token_segment_id] * padding_length
									label_ids += [pad_token_label_id] * padding_length

								assert len(A_   ) == max_seq_length
								assert len(A_   ) == max_seq_length
								assert len(A_   ) == max_seq_length
								assert len(A_   ) == max_seq_length

								if ex_index < 5:
									logger.info('*** Example ***'   )
									logger.info('guid: %s'	,example.guid   )
									logger.info('tokens: %s'	,' '.join([str(A_   ) for x in tokens]   )   )
									logger.info('input_ids: %s'	,' '.join([str(A_   ) for x in input_ids]   )   )
									logger.info('input_mask: %s'	,' '.join([str(A_   ) for x in input_mask]   )   )
									logger.info('segment_ids: %s'	,' '.join([str(A_   ) for x in segment_ids]   )   )
									logger.info('label_ids: %s'	,' '.join([str(A_   ) for x in label_ids]   )   )

								if "token_type_ids" not in tokenizer.model_input_names:
									A     = None

								features.append(
								    InputFeatures(
								        input_ids=A_	,attention_mask=A_	,token_type_ids=A_	,label_ids=A_   )   )
							return features


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset







		class 					lowerCAmelCase_					(   _lowercase     ):



								'''simple docstring'''


								_lowerCamelCase:     List[InputFeatures]
								_lowerCamelCase:     int   =     nn.CrossEntropyLoss().ignore_index


								def __init__(			self  :   Tuple	,A_  :   TokenClassificationTask	,A_  :   str	,A_  :   PreTrainedTokenizer	,A_  :   List[str]	,A_  :   str	,A_  :   Optional[int] = None	,A_  :   Optional[Any]=False	,A_  :   Split = Split.train	,)  -> Union[str, Any]:
									# Load data features from cache or dataset file
									A     = os.path.join(
									    A_	,'cached_{}_{}_{}'.format(mode.value	,tokenizer.__class__.__name__	,str(A_   )   )	,)

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									A     = cached_features_file + '.lock'
									with FileLock(A_   ):
										if os.path.exists(A_   ) and not overwrite_cache:
											logger.info(F'Loading features from cached file {cached_features_file}'   )
											A     = torch.load(A_   )
										else:
											logger.info(F'Creating features from dataset file at {data_dir}'   )
											A     = token_classification_task.read_examples_from_file(A_	,A_   )
											# TODO clean up all this to leverage built-in features of tokenizers
											A     = token_classification_task.convert_examples_to_features(
											    A_	,A_	,A_	,A_	,cls_token_at_end=bool(model_type in ['xlnet']   )	,cls_token=tokenizer.cls_token	,cls_token_segment_id=2 if model_type in ['xlnet'] else 0	,sep_token=tokenizer.sep_token	,sep_token_extra=A_	,pad_on_left=bool(tokenizer.padding_side == 'left'   )	,pad_token=tokenizer.pad_token_id	,pad_token_segment_id=tokenizer.pad_token_type_id	,pad_token_label_id=self.pad_token_label_id	,)
											logger.info(F'Saving features into cached file {cached_features_file}'   )
											torch.save(self.features	,A_   )


								def __len__(			self  :   List[str]   )  -> str:
									return len(self.features   )




								def __getitem__(			self  :   List[str]	,A_  :   int   )  -> InputFeatures:
									return self.features[i]


if is_tf_available():
		import tensorflow as tf







		class 					lowerCAmelCase_					:



								'''simple docstring'''


								_lowerCamelCase:     List[InputFeatures]
								_lowerCamelCase:     int   =     -100


								def __init__(			self  :   List[Any]	,A_  :   TokenClassificationTask	,A_  :   str	,A_  :   PreTrainedTokenizer	,A_  :   List[str]	,A_  :   str	,A_  :   Optional[int] = None	,A_  :   Union[str, Any]=False	,A_  :   Split = Split.train	,)  -> Union[str, Any]:
									A     = token_classification_task.read_examples_from_file(A_	,A_   )
									# TODO clean up all this to leverage built-in features of tokenizers
									A     = token_classification_task.convert_examples_to_features(
									    A_	,A_	,A_	,A_	,cls_token_at_end=bool(model_type in ['xlnet']   )	,cls_token=tokenizer.cls_token	,cls_token_segment_id=2 if model_type in ['xlnet'] else 0	,sep_token=tokenizer.sep_token	,sep_token_extra=A_	,pad_on_left=bool(tokenizer.padding_side == 'left'   )	,pad_token=tokenizer.pad_token_id	,pad_token_segment_id=tokenizer.pad_token_type_id	,pad_token_label_id=self.pad_token_label_id	,)

									def gen():
										for ex in self.features:
											if ex.token_type_ids is None:
												yield (
												    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
												    ex.label_ids,
												)
											else:
												yield (
												    {
												        "input_ids": ex.input_ids,
												        "attention_mask": ex.attention_mask,
												        "token_type_ids": ex.token_type_ids,
												    },
												    ex.label_ids,
												)

									if "token_type_ids" not in tokenizer.model_input_names:
										A     = tf.data.Dataset.from_generator(
										    A_	,({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)	,(
										        {'input_ids': tf.TensorShape([None]   ), 'attention_mask': tf.TensorShape([None]   )},
										        tf.TensorShape([None]   ),
										    )	,)
									else:
										A     = tf.data.Dataset.from_generator(
										    A_	,({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)	,(
										        {
										            'input_ids': tf.TensorShape([None]   ),
										            'attention_mask': tf.TensorShape([None]   ),
										            'token_type_ids': tf.TensorShape([None]   ),
										        },
										        tf.TensorShape([None]   ),
										    )	,)


								def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> List[Any]:
									A     = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

									return self.dataset


								def __len__(			self  :   Optional[int]   )  -> Optional[Any]:
									return len(self.features   )




								def __getitem__(			self  :   Optional[Any]	,A_  :   Union[str, Any]   )  -> InputFeatures:
									return self.features[i]

"""simple docstring"""


from __future__ import annotations






def    _snake_case					(		snake_case__ :  tuple[int, int]						,   snake_case__ :  int  ):
	A ,   A     = position
	A     = [
	    (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),
	]
	A     = []

	for position in positions:
		A ,   A     = position
		if 0 <= y_test < n and 0 <= x_test < n:
			permissible_positions.append(snake_case__  )

	return permissible_positions






def    _snake_case					(		snake_case__ :  list[list[int]]  ):
	return not any(elem == 0 for row in board for elem in row  )






def    _snake_case					(		snake_case__ :  list[list[int]]						,   snake_case__ :  tuple[int, int]						,   snake_case__ :  int  ):

	if is_complete(snake_case__  ):
		return True

	for position in get_valid_pos(snake_case__						,   len(snake_case__  )  ):
		A ,   A     = position

		if board[y][x] == 0:
			A     = curr + 1
			if open_knight_tour_helper(snake_case__						,   snake_case__						,   curr + 1  ):
				return True
			A     = 0

	return False






def    _snake_case					(		snake_case__ :  int  ):
	A     = [[0 for i in range(snake_case__  )] for j in range(snake_case__  )]

	for i in range(snake_case__  ):
		for j in range(snake_case__  ):
			A     = 1
			if open_knight_tour_helper(snake_case__						,   (i, j)						,   1  ):
				return board
			A     = 0

	A     = F'Open Kight Tour cannot be performed on a board of size {n}'
	raise ValueError(snake_case__  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()