





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


UpperCAmelCase_							=				logging.getLogger(__name__)
@dataclass
class     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       str
		__a       :       List[str]
		__a       :       Optional[List[str]]




@dataclass
class     UpperCamelCase__					:






		'''simple docstring'''






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




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Any       =	"""train"""
		__a       :       List[str]       =	"""dev"""
		__a       :       int       =	"""test"""




class     UpperCamelCase__					:






		'''simple docstring'''






		@staticmethod
		def 						snake_case__				(							snake_case__,   snake_case__  )       -> List[InputExample]:






									"""simple docstring"""
									raise NotImplementedError






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






									"""simple docstring"""
									raise NotImplementedError






		@staticmethod
		def 						snake_case__				(							snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__=False,   snake_case__="[CLS]",   snake_case__=1,   snake_case__="[SEP]",   snake_case__=False,   snake_case__=False,   snake_case__=0,   snake_case__=0,   snake_case__=-1_00,   snake_case__=0,   snake_case__=True,   )       -> List[InputFeatures]:






									"""simple docstring"""
									lowercase_	:   str  				=     {label: i for i, label in enumerate(snake_case__  )}

									lowercase_	:   Optional[Any]  				=     []
									for ex_index, example in enumerate(snake_case__  ):
																if ex_index % 1_00_00 == 0:
																							logger.info("""Writing example %d of %d""",   snake_case__,   len(snake_case__  )  )

																lowercase_	:   Dict  				=     []
																lowercase_	:   Optional[int]  				=     []
																for word, label in zip(example.words,   example.labels  ):
																							lowercase_	:   Dict  				=     tokenizer.tokenize(snake_case__  )

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																lowercase_	:   Any  				=     tokenizer.num_special_tokens_to_add()
																if len(snake_case__  ) > max_seq_length - special_tokens_count:
																							lowercase_	:   List[Any]  				=     tokens[: (max_seq_length - special_tokens_count)]
																							lowercase_	:   int  				=     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]
																lowercase_	:   Union[str, Any]  				=     [sequence_a_segment_id] * len(snake_case__  )

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

																lowercase_	:   int  				=     tokenizer.convert_tokens_to_ids(snake_case__  )

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

																# Zero-pad up to the sequence length.
																lowercase_	:   Optional[int]  				=     max_seq_length - len(snake_case__  )
																if pad_on_left:
																							lowercase_	:   List[Any]  				=     ([pad_token] * padding_length) + input_ids
																							lowercase_	:   Any  				=     ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																							lowercase_	:   List[Any]  				=     ([pad_token_segment_id] * padding_length) + segment_ids
																							lowercase_	:   Union[str, Any]  				=     ([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(snake_case__  ) == max_seq_length
																assert len(snake_case__  ) == max_seq_length
																assert len(snake_case__  ) == max_seq_length
																assert len(snake_case__  ) == max_seq_length

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

																if "token_type_ids" not in tokenizer.model_input_names:
																							lowercase_	:   Dict  				=     None

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


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






									'''simple docstring'''






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






									def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = None,   snake_case__=False,   snake_case__ = Split.train,   )       -> Union[str, Any]:






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

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






									def __len__(							self  )       -> Optional[Any]:






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






									def __getitem__(							self,   snake_case__  )       -> InputFeatures:






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


if is_tf_available():
							import tensorflow as tf
							class     UpperCamelCase__					:






									'''simple docstring'''






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






									def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = None,   snake_case__=False,   snake_case__ = Split.train,   )       -> Union[str, Any]:






																"""simple docstring"""
																lowercase_	:   Optional[Any]  				=     token_classification_task.read_examples_from_file(snake_case__,   snake_case__  )
																# TODO clean up all this to leverage built-in features of tokenizers
																lowercase_	:   Optional[Any]  				=     token_classification_task.convert_examples_to_features(
																    snake_case__,   snake_case__,   snake_case__,   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=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:
																							lowercase_	:   int  				=     tf.data.Dataset.from_generator(
																							    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:
																							lowercase_	:   Any  				=     tf.data.Dataset.from_generator(
																							    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 						snake_case__				(							self  )       -> str:






																"""simple docstring"""
																lowercase_	:   Tuple  				=     self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

																return self.dataset






									def __len__(							self  )       -> int:






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






									def __getitem__(							self,   snake_case__  )       -> InputFeatures:






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






def  __magic_name__  (				lowercase				)				-> list:

							"""simple docstring"""



							if n_term == "":
														return []
							lowercase_	:   list  				=     []
							for temp in range(int(lowercase				)				):
														series.append(f"""1/{temp + 1}""" if series else """1"""				)
							return series


if __name__ == "__main__":
							UpperCAmelCase_							=				input("""Enter the last number (nth term) of the Harmonic Series""")
							print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
							print(harmonic_series(nth_term))