





'''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_   :   str       =					logging.getLogger(__name__)






@dataclass
class       UpperCamelCase__  :
	lowerCAmelCase__					:			str
	lowerCAmelCase__					:			List[str]
	lowerCAmelCase__					:			Optional[List[str]]






@dataclass
class       UpperCamelCase__  :
	lowerCAmelCase__					:			List[int]
	lowerCAmelCase__					:			List[int]
	lowerCAmelCase__					:			Optional[List[int]]								=						None
	lowerCAmelCase__					:			Optional[List[int]]								=						None






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			List[str]								=						"train"
	lowerCAmelCase__					:			Optional[Any]								=						"dev"
	lowerCAmelCase__					:			Optional[int]								=						"test"






class       UpperCamelCase__  :







	@staticmethod
	def 						__a   (				lowerCamelCase			:   List[Any] , lowerCamelCase			:   Union[Split, str]		):




				'''simple docstring'''
				raise NotImplementedError







	@staticmethod
	def 						__a   (				lowerCamelCase			:   str		):




				'''simple docstring'''
				raise NotImplementedError







	@staticmethod
	def 						__a   (				lowerCamelCase			:   List[InputExample] , lowerCamelCase			:   List[str] , lowerCamelCase			:   int , lowerCamelCase			:   PreTrainedTokenizer , lowerCamelCase			:   List[str]=False , lowerCamelCase			:   Dict="[CLS]" , lowerCamelCase			:   int=1 , lowerCamelCase			:   int="[SEP]" , lowerCamelCase			:   str=False , lowerCamelCase			:   Union[str, Any]=False , lowerCamelCase			:   Union[str, Any]=0 , lowerCamelCase			:   Dict=0 , lowerCamelCase			:   Any=-1_0_0 , lowerCamelCase			:   Any=0 , lowerCamelCase			:   List[str]=True , ):




				'''simple docstring'''
				a__     = {label: i for i, label in enumerate(lowerCamelCase		)}

				a__     = []
				for ex_index, example in enumerate(lowerCamelCase		):
							if ex_index % 1_0_0_0_0 == 0:
										logger.info("Writing example %d of %d" , lowerCamelCase , len(lowerCamelCase		)		)

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

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
							a__     = tokenizer.num_special_tokens_to_add()
							if len(lowerCamelCase		) > 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(lowerCamelCase		)

							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(lowerCamelCase		)

							# 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(lowerCamelCase		)

							# Zero-pad up to the sequence length.
							a__     = max_seq_length - len(lowerCamelCase		)
							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(lowerCamelCase		) == max_seq_length
							assert len(lowerCamelCase		) == max_seq_length
							assert len(lowerCamelCase		) == max_seq_length
							assert len(lowerCamelCase		) == max_seq_length

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

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

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


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






		class       UpperCamelCase__  (		__lowerCAmelCase     ):
			lowerCAmelCase__					:			List[InputFeatures]
			lowerCAmelCase__					:			int								=						nn.CrossEntropyLoss().ignore_index







			def __init__(				self			:   Dict , lowerCamelCase			:   TokenClassificationTask , lowerCamelCase			:   str , lowerCamelCase			:   PreTrainedTokenizer , lowerCamelCase			:   List[str] , lowerCamelCase			:   str , lowerCamelCase			:   Optional[int] = None , lowerCamelCase			:   Optional[Any]=False , lowerCamelCase			:   Split = Split.train , ):




						'''simple docstring'''
						# Load data features from cache or dataset file
						a__     = os.path.join(
						    lowerCamelCase , "cached_{}_{}_{}".format(mode.value , tokenizer.__class__.__name__ , str(lowerCamelCase		)		) , )

						# 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(lowerCamelCase		):
									if os.path.exists(lowerCamelCase		) and not overwrite_cache:
												logger.info(F'''Loading features from cached file {cached_features_file}'''		)
												a__     = torch.load(lowerCamelCase		)
									else:
												logger.info(F'''Creating features from dataset file at {data_dir}'''		)
												a__     = token_classification_task.read_examples_from_file(lowerCamelCase , lowerCamelCase		)
												# TODO clean up all this to leverage built-in features of tokenizers
												a__     = token_classification_task.convert_examples_to_features(
												    lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , 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=lowerCamelCase , 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 , lowerCamelCase		)







			def __len__(				self			:   Tuple		):




						'''simple docstring'''
						return len(self.features		)







			def __getitem__(				self			:   Dict , lowerCamelCase			:   Any		):




						'''simple docstring'''
						return self.features[i]


if is_tf_available():
		import tensorflow as tf






		class       UpperCamelCase__  :
			lowerCAmelCase__					:			List[InputFeatures]
			lowerCAmelCase__					:			int								=						-100







			def __init__(				self			:   Optional[int] , lowerCamelCase			:   TokenClassificationTask , lowerCamelCase			:   str , lowerCamelCase			:   PreTrainedTokenizer , lowerCamelCase			:   List[str] , lowerCamelCase			:   str , lowerCamelCase			:   Optional[int] = None , lowerCamelCase			:   Tuple=False , lowerCamelCase			:   Split = Split.train , ):




						'''simple docstring'''
						a__     = token_classification_task.read_examples_from_file(lowerCamelCase , lowerCamelCase		)
						# TODO clean up all this to leverage built-in features of tokenizers
						a__     = token_classification_task.convert_examples_to_features(
						    lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , 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=lowerCamelCase , 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(
									    lowerCamelCase , ({"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(
									    lowerCamelCase , ({"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 						__a   (				self			:   Dict		):




						'''simple docstring'''
						a__     = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

						return self.dataset







			def __len__(				self			:   Tuple		):




						'''simple docstring'''
						return len(self.features		)







			def __getitem__(				self			:   Dict , lowerCamelCase			:   Tuple		):




						'''simple docstring'''
						return self.features[i]










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     int = 400_0000     )   ->    int:
			a__     = [0, 1]
			a__     = 0
			while fib[i] <= n:
						fib.append(fib[i] + fib[i + 1]     )
						if fib[i + 2] > n:
									break
						i += 1
			a__     = 0
			for j in range(len(__lowerCamelCase     ) - 1     ):
						if fib[j] % 2 == 0:
									total += fib[j]

			return total


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



