
"""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


lowerCAmelCase__				=					logging.getLogger(__name__)





@dataclass
class 			snake_case  :
							UpperCAmelCase__      =   42
							UpperCAmelCase__      =   42
							UpperCAmelCase__      =   42







@dataclass
class 			snake_case  :
							UpperCAmelCase__      =   42
							UpperCAmelCase__      =   42
							UpperCAmelCase__      =   None
							UpperCAmelCase__      =   None







class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   '''train'''
							UpperCAmelCase__      =   '''dev'''
							UpperCAmelCase__      =   '''test'''







class 			snake_case  :

							@staticmethod
							def _lowercase       (SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									raise NotImplementedError

							@staticmethod
							def _lowercase       (SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									raise NotImplementedError







							@staticmethod
							def _lowercase       (SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_="[CLS]"							,							SCREAMING_SNAKE_CASE_=1							,							SCREAMING_SNAKE_CASE_="[SEP]"							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_=0							,							SCREAMING_SNAKE_CASE_=0							,							SCREAMING_SNAKE_CASE_=-1_00							,							SCREAMING_SNAKE_CASE_=0							,							SCREAMING_SNAKE_CASE_=True							,							):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_     )}

									SCREAMING_SNAKE_CASE_    			=			[]
									for ex_index, example in enumerate(SCREAMING_SNAKE_CASE_     ):
											if ex_index % 1_00_00 == 0:
													logger.info('''Writing example %d of %d'''							,							SCREAMING_SNAKE_CASE_							,							len(SCREAMING_SNAKE_CASE_     )     )

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

													# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
													if len(SCREAMING_SNAKE_CASE_     ) > 0:
															tokens.extend(SCREAMING_SNAKE_CASE_     )
															# 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(SCREAMING_SNAKE_CASE_     ) - 1)     )

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

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

											SCREAMING_SNAKE_CASE_    			=			tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_     )

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

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

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

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

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


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





						class 			snake_case  (				__lowercase ):
													UpperCAmelCase__      =   42
													UpperCAmelCase__      =   nn.CrossEntropyLoss().ignore_index

													def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_ = Split.train							,							):

															"""simple docstring"""







															SCREAMING_SNAKE_CASE_    			=			os.path.join(
															    SCREAMING_SNAKE_CASE_							,							'''cached_{}_{}_{}'''.format(mode.value							,							tokenizer.__class__.__name__							,							str(SCREAMING_SNAKE_CASE_     )     )							,							)

															# Make sure only the first process in distributed training processes the dataset,
															# and the others will use the cache.
															SCREAMING_SNAKE_CASE_    			=			cached_features_file + '''.lock'''
															with FileLock(SCREAMING_SNAKE_CASE_     ):
																	if os.path.exists(SCREAMING_SNAKE_CASE_     ) and not overwrite_cache:
																			logger.info(f'Loading features from cached file {cached_features_file}'     )
																			SCREAMING_SNAKE_CASE_    			=			torch.load(SCREAMING_SNAKE_CASE_     )
																	else:
																			logger.info(f'Creating features from dataset file at {data_dir}'     )
																			SCREAMING_SNAKE_CASE_    			=			token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
																			# TODO clean up all this to leverage built-in features of tokenizers
																			SCREAMING_SNAKE_CASE_    			=			token_classification_task.convert_examples_to_features(
																			    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							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=SCREAMING_SNAKE_CASE_							,							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							,							SCREAMING_SNAKE_CASE_     )

													def __len__(self     ):

															"""simple docstring"""







															return len(self.features     )







													def __getitem__(self							,							SCREAMING_SNAKE_CASE_     ):

															"""simple docstring"""







															return self.features[i]


if is_tf_available():
						import tensorflow as tf





						class 			snake_case  :
													UpperCAmelCase__      =   42
													UpperCAmelCase__      =   -100

													def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_ = None							,							SCREAMING_SNAKE_CASE_=False							,							SCREAMING_SNAKE_CASE_ = Split.train							,							):

															"""simple docstring"""







															SCREAMING_SNAKE_CASE_    			=			token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )
															# TODO clean up all this to leverage built-in features of tokenizers
															SCREAMING_SNAKE_CASE_    			=			token_classification_task.convert_examples_to_features(
															    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							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=SCREAMING_SNAKE_CASE_							,							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:
																	SCREAMING_SNAKE_CASE_    			=			tf.data.Dataset.from_generator(
																	    SCREAMING_SNAKE_CASE_							,							({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)							,							(
																	        {'''input_ids''': tf.TensorShape([None]     ), '''attention_mask''': tf.TensorShape([None]     )},
																	        tf.TensorShape([None]     ),
																	    )							,							)
															else:
																	SCREAMING_SNAKE_CASE_    			=			tf.data.Dataset.from_generator(
																	    SCREAMING_SNAKE_CASE_							,							({'''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 _lowercase       (self     ):

															"""simple docstring"""







															SCREAMING_SNAKE_CASE_    			=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features     )     )     )

															return self.dataset

													def __len__(self     ):

															"""simple docstring"""







															return len(self.features     )







													def __getitem__(self							,							SCREAMING_SNAKE_CASE_     ):

															"""simple docstring"""







															return self.features[i]

"""simple docstring"""




def 							_lowerCamelCase			(			__a     ):
		if divisor % 5 == 0 or divisor % 2 == 0:
				return 0
		SCREAMING_SNAKE_CASE_    			=			1
		SCREAMING_SNAKE_CASE_    			=			1
		while repunit:
				SCREAMING_SNAKE_CASE_    			=			(10 * repunit + 1) % divisor
				repunit_index += 1
		return repunit_index



def 							_lowerCamelCase			(			__a = 1_000_000     ):
		SCREAMING_SNAKE_CASE_    			=			limit - 1
		if divisor % 2 == 0:
				divisor += 1
		while least_divisible_repunit(__a     ) <= limit:
				divisor += 2
		return divisor


if __name__ == "__main__":
						print(f'''{solution() = }''')