





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


A    :					Union[str, Any] =      logging.getLogger(__name__)


@dataclass
class   A			:
			'''simple docstring'''




			__lowerCamelCase	:       str
			__lowerCamelCase	:       List[str]
			__lowerCamelCase	:       Optional[List[str]]


@dataclass
class   A			:
			'''simple docstring'''




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


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Dict							=    '''train'''
			__lowerCamelCase	:       int							=    '''dev'''
			__lowerCamelCase	:       Optional[Any]							=    '''test'''


class   A			:
			'''simple docstring'''




			@staticmethod
			def    a_       (			__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    Union[Split, str]   )	->	List[InputExample]:







					"""simple docstring"""






					raise NotImplementedError






			@staticmethod
			def    a_       (			__lowerCAmelCase   :    str   )	->	List[str]:







					"""simple docstring"""






					raise NotImplementedError







			@staticmethod
			def    a_       (			__lowerCAmelCase   :    List[InputExample]							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    PreTrainedTokenizer							,	__lowerCAmelCase   :    Optional[Any]=False							,	__lowerCAmelCase   :    str="[CLS]"							,	__lowerCAmelCase   :    Dict=1							,	__lowerCAmelCase   :    Tuple="[SEP]"							,	__lowerCAmelCase   :    List[Any]=False							,	__lowerCAmelCase   :    List[Any]=False							,	__lowerCAmelCase   :    Optional[Any]=0							,	__lowerCAmelCase   :    List[Any]=0							,	__lowerCAmelCase   :    Tuple=-1_00							,	__lowerCAmelCase   :    Union[str, Any]=0							,	__lowerCAmelCase   :    Any=True							,	)	->	List[InputFeatures]:







					"""simple docstring"""






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

					A__        =   []
					for ex_index, example in enumerate(__lowerCAmelCase   ):
							if ex_index % 1_00_00 == 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   A			(SCREAMING_SNAKE_CASE    ):
								'''simple docstring'''




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






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







										"""simple docstring"""






										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   :    List[str]   )	->	Any:







										"""simple docstring"""






										return len(self.features   )







								def __getitem__(			self   :    List[str]							,	__lowerCAmelCase   :    Tuple   )	->	InputFeatures:







										"""simple docstring"""






										return self.features[i]


if is_tf_available():
					import tensorflow as tf


					class   A			:
								'''simple docstring'''




								__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   :    Optional[int]=False							,	__lowerCAmelCase   :    Split = Split.train							,	)	->	int:







										"""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   )	->	Any:







										"""simple docstring"""






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

										return self.dataset






								def __len__(			self   :    Tuple   )	->	List[str]:







										"""simple docstring"""






										return len(self.features   )







								def __getitem__(			self   :    int							,	__lowerCAmelCase   :    Union[str, Any]   )	->	InputFeatures:







										"""simple docstring"""






										return self.features[i]










def    __lowerCamelCase					(							__a   :int = 1_0_0_0_0_0_0    )		->				int:






		"""simple docstring"""

		A__        =   limit + 1
		A__        =   [0] * limit
		for first_term in range(1    ,     __a    ):
				for n in range(__a    ,     __a    ,     __a    ):
						A__        =   first_term + n / first_term
						if common_difference % 4:  # d must be divisble by 4
								continue
						else:
								common_difference /= 4
								if (
								    first_term > common_difference
								    and first_term < 4 * common_difference
								):  # since x,y,z are positive integers
										frequency[n] += 1  # so z>0 and a>d ,also 4d<a

		A__        =   sum(1 for x in frequency[1:limit] if x == 1_0    )

		return count


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



