



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


SCREAMING_SNAKE_CASE     				=						logging.getLogger(__name__)



@dataclass
class 	lowerCamelCase      :
					lowerCAmelCase_				:    int									=  42
					lowerCAmelCase_				:    Tuple									=  42
					lowerCAmelCase_				:    str									=  42





@dataclass
class 	lowerCamelCase      :
					lowerCAmelCase_				:    Optional[Any]									=  42
					lowerCAmelCase_				:    Optional[Any]									=  42
					lowerCAmelCase_				:    List[Any]									=  None
					lowerCAmelCase_				:    Dict									=  None





class 	lowerCamelCase      (						lowercase__  ):
					lowerCAmelCase_				:    Dict									=  'train'
					lowerCAmelCase_				:    Dict									=  'dev'
					lowerCAmelCase_				:    str									=  'test'





class 	lowerCamelCase      :



					@staticmethod
					def    A__		(			lowerCAmelCase    ,	lowerCAmelCase     ):
							raise NotImplementedError



					@staticmethod
					def    A__		(			lowerCAmelCase     ):
							raise NotImplementedError



					@staticmethod
					def    A__		(			lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=False    ,	lowerCAmelCase="[CLS]"    ,	lowerCAmelCase=1    ,	lowerCAmelCase="[SEP]"    ,	lowerCAmelCase=False    ,	lowerCAmelCase=False    ,	lowerCAmelCase=0    ,	lowerCAmelCase=0    ,	lowerCAmelCase=-100    ,	lowerCAmelCase=0    ,	lowerCAmelCase=True    ,	):
							UpperCAmelCase_						=				{label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE     )}

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

									UpperCAmelCase_						=				[]
									UpperCAmelCase_						=				[]
									for word, label in zip(example.words    ,	example.labels     ):
											UpperCAmelCase_						=				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.
									UpperCAmelCase_						=				tokenizer.num_special_tokens_to_add()
									if len(_SCREAMING_SNAKE_CASE     ) > max_seq_length - special_tokens_count:
											UpperCAmelCase_						=				tokens[: (max_seq_length - special_tokens_count)]
											UpperCAmelCase_						=				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]
									UpperCAmelCase_						=				[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:
											UpperCAmelCase_						=				[cls_token] + tokens
											UpperCAmelCase_						=				[pad_token_label_id] + label_ids
											UpperCAmelCase_						=				[cls_token_segment_id] + segment_ids

									UpperCAmelCase_						=				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.
									UpperCAmelCase_						=				[1 if mask_padding_with_zero else 0] * len(_SCREAMING_SNAKE_CASE     )

									# Zero-pad up to the sequence length.
									UpperCAmelCase_						=				max_seq_length - len(_SCREAMING_SNAKE_CASE     )
									if pad_on_left:
											UpperCAmelCase_						=				([pad_token] * padding_length) + input_ids
											UpperCAmelCase_						=				([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
											UpperCAmelCase_						=				([pad_token_segment_id] * padding_length) + segment_ids
											UpperCAmelCase_						=				([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:
											UpperCAmelCase_						=				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 	lowerCamelCase      (						lowercase__  ):
								lowerCAmelCase_				:    str									=  42
								lowerCAmelCase_				:    Tuple									=  nn.CrossEntropyLoss().ignore_index



								def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase=False    ,	lowerCAmelCase = Split.train    ,	):
										UpperCAmelCase_						=				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.
										UpperCAmelCase_						=				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}'''     )
														UpperCAmelCase_						=				torch.load(_SCREAMING_SNAKE_CASE     )
												else:
														logger.info(f'''Creating features from dataset file at {data_dir}'''     )
														UpperCAmelCase_						=				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
														UpperCAmelCase_						=				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     ):
										return len(self.features     )



								def __getitem__(			self    ,	lowerCAmelCase     ):
										return self.features[i]


if is_tf_available():
			import tensorflow as tf



			class 	lowerCamelCase      :
								lowerCAmelCase_				:    Optional[int]									=  42
								lowerCAmelCase_				:    Tuple									=  -100



								def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = None    ,	lowerCAmelCase=False    ,	lowerCAmelCase = Split.train    ,	):
										UpperCAmelCase_						=				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
										UpperCAmelCase_						=				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:
												UpperCAmelCase_						=				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:
												UpperCAmelCase_						=				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    A__		(			self     ):
										UpperCAmelCase_						=				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features     )     )     )

										return self.dataset



								def __len__(			self     ):
										return len(self.features     )



								def __getitem__(			self    ,	lowerCAmelCase     ):
										return self.features[i]






import math







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							list[int]:
		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				2
		UpperCAmelCase_						=				int(math.sqrt(__SCREAMING_SNAKE_CASE ) )  # Size of every segment
		UpperCAmelCase_						=				[True] * (end + 1)
		UpperCAmelCase_						=				[]

		while start <= end:
				if temp[start] is True:
						in_prime.append(__SCREAMING_SNAKE_CASE )
						for i in range(start * start			,     end + 1			,     __SCREAMING_SNAKE_CASE ):
								UpperCAmelCase_						=				False
				start += 1
		prime += in_prime

		UpperCAmelCase_						=				end + 1
		UpperCAmelCase_						=				min(2 * end			,     __SCREAMING_SNAKE_CASE )

		while low <= n:
				UpperCAmelCase_						=				[True] * (high - low + 1)
				for each in in_prime:
						UpperCAmelCase_						=				math.floor(low / each ) * each
						if t < low:
								t += each

						for j in range(__SCREAMING_SNAKE_CASE			,     high + 1			,     __SCREAMING_SNAKE_CASE ):
								UpperCAmelCase_						=				False

				for j in range(len(__SCREAMING_SNAKE_CASE ) ):
						if temp[j] is True:
								prime.append(j + low )

				UpperCAmelCase_						=				high + 1
				UpperCAmelCase_						=				min(high + end			,     __SCREAMING_SNAKE_CASE )

		return prime

print(sieve(10**6))

