


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_A							     =       logging.get_logger(__name__)

@dataclass
class 			lowerCamelCase_					:
							_lowerCamelCase				: str
							_lowerCamelCase				: str
							_lowerCamelCase				: Optional[str]       =					None
							_lowerCamelCase				: Optional[str]       =					None





							def   __magic_name__      (							self    ):
								return json.dumps(dataclasses.asdict(self    )			,       indent=2    ) + "\n"







@dataclass(frozen=_SCREAMING_SNAKE_CASE    )
class 			lowerCamelCase_					:
							_lowerCamelCase				: List[int]
							_lowerCamelCase				: Optional[List[int]]       =					None
							_lowerCamelCase				: Optional[List[int]]       =					None
							_lowerCamelCase				: Optional[Union[int, float]]       =					None





							def   __magic_name__      (							self    ):
								return json.dumps(dataclasses.asdict(self    )    ) + "\n"







class 			lowerCamelCase_					:





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								raise NotImplementedError()





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								raise NotImplementedError()





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								raise NotImplementedError()





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								raise NotImplementedError()





							def   __magic_name__      (							self    ):
								raise NotImplementedError()





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								if len(self.get_labels()    ) > 1:
									a_     			=   self.get_labels()[int(example.label    )]
								return example





							@classmethod
							def   __magic_name__      (							cls			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None    ):
								with open(_SCREAMING_SNAKE_CASE			,       """r"""			,       encoding="""utf-8-sig"""    ) as f:
									return list(csv.reader(_SCREAMING_SNAKE_CASE			,       delimiter="""\t"""			,       quotechar=_SCREAMING_SNAKE_CASE    )    )







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							def __init__(							self			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE="classification"			,       _SCREAMING_SNAKE_CASE=False    ):
								a_     			=   [] if labels is None else labels
								a_     			=   [] if examples is None else examples
								a_     			=   mode
								a_     			=   verbose





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





							def __getitem__(							self			,       _SCREAMING_SNAKE_CASE    ):
								if isinstance(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									return SingleSentenceClassificationProcessor(labels=self.labels			,       examples=self.examples[idx]    )
								return self.examples[idx]





							@classmethod
							def   __magic_name__      (							cls			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=""			,       _SCREAMING_SNAKE_CASE=0			,       _SCREAMING_SNAKE_CASE=1			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=False			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   cls(**_SCREAMING_SNAKE_CASE    )
								processor.add_examples_from_csv(
								    _SCREAMING_SNAKE_CASE			,       split_name=_SCREAMING_SNAKE_CASE			,       column_label=_SCREAMING_SNAKE_CASE			,       column_text=_SCREAMING_SNAKE_CASE			,       column_id=_SCREAMING_SNAKE_CASE			,       skip_first_row=_SCREAMING_SNAKE_CASE			,       overwrite_labels=_SCREAMING_SNAKE_CASE			,       overwrite_examples=_SCREAMING_SNAKE_CASE			,       )
								return processor





							@classmethod
							def   __magic_name__      (							cls			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       **_SCREAMING_SNAKE_CASE    ):
								a_     			=   cls(**_SCREAMING_SNAKE_CASE    )
								processor.add_examples(_SCREAMING_SNAKE_CASE			,       labels=_SCREAMING_SNAKE_CASE    )
								return processor





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=""			,       _SCREAMING_SNAKE_CASE=0			,       _SCREAMING_SNAKE_CASE=1			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=False			,       _SCREAMING_SNAKE_CASE=False			,       _SCREAMING_SNAKE_CASE=False			,       ):
								a_     			=   self._read_tsv(_SCREAMING_SNAKE_CASE    )
								if skip_first_row:
									a_     			=   lines[1:]
								a_     			=   []
								a_     			=   []
								a_     			=   []
								for i, line in enumerate(_SCREAMING_SNAKE_CASE    ):
									texts.append(line[column_text]    )
									labels.append(line[column_label]    )
									if column_id is not None:
										ids.append(line[column_id]    )
									else:
										a_     			=   f"""{split_name}-{i}""" if split_name else str(_SCREAMING_SNAKE_CASE    )
										ids.append(_SCREAMING_SNAKE_CASE    )

								return self.add_examples(
								    _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       overwrite_labels=_SCREAMING_SNAKE_CASE			,       overwrite_examples=_SCREAMING_SNAKE_CASE    )





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=False			,       _SCREAMING_SNAKE_CASE=False    ):
								if labels is not None and len(_SCREAMING_SNAKE_CASE    ) != len(_SCREAMING_SNAKE_CASE    ):
									raise ValueError(
									    f"""Text and labels have mismatched lengths {len(_SCREAMING_SNAKE_CASE    )} and {len(_SCREAMING_SNAKE_CASE    )}"""    )
								if ids is not None and len(_SCREAMING_SNAKE_CASE    ) != len(_SCREAMING_SNAKE_CASE    ):
									raise ValueError(f"""Text and ids have mismatched lengths {len(_SCREAMING_SNAKE_CASE    )} and {len(_SCREAMING_SNAKE_CASE    )}"""    )
								if ids is None:
									a_     			=   [None] * len(_SCREAMING_SNAKE_CASE    )
								if labels is None:
									a_     			=   [None] * len(_SCREAMING_SNAKE_CASE    )
								a_     			=   []
								a_     			=   set()
								for text_or_text_and_label, label, guid in zip(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
									if isinstance(_SCREAMING_SNAKE_CASE			,       (tuple, list)    ) and label is None:
										a_			, a_     			=   text_or_text_and_label
									else:
										a_     			=   text_or_text_and_label
									added_labels.add(_SCREAMING_SNAKE_CASE    )
									examples.append(InputExample(guid=_SCREAMING_SNAKE_CASE			,       text_a=_SCREAMING_SNAKE_CASE			,       text_b=_SCREAMING_SNAKE_CASE			,       label=_SCREAMING_SNAKE_CASE    )    )

								# Update examples
								if overwrite_examples:
									a_     			=   examples
								else:
									self.examples.extend(_SCREAMING_SNAKE_CASE    )

								# Update labels
								if overwrite_labels:
									a_     			=   list(_SCREAMING_SNAKE_CASE    )
								else:
									a_     			=   list(set(self.labels    ).union(_SCREAMING_SNAKE_CASE    )    )

								return self.examples





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=False			,       _SCREAMING_SNAKE_CASE=0			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=None			,       ):
								if max_length is None:
									a_     			=   tokenizer.max_len

								a_     			=   {label: i for i, label in enumerate(self.labels    )}

								a_     			=   []
								for ex_index, example in enumerate(self.examples    ):
									if ex_index % 1_0000 == 0:
										logger.info(f"""Tokenizing example {ex_index}"""    )

									a_     			=   tokenizer.encode(
									    example.text_a			,       add_special_tokens=_SCREAMING_SNAKE_CASE			,       max_length=min(_SCREAMING_SNAKE_CASE			,       tokenizer.max_len    )			,       )
									all_input_ids.append(_SCREAMING_SNAKE_CASE    )

								a_     			=   max(len(_SCREAMING_SNAKE_CASE    ) for input_ids in all_input_ids    )

								a_     			=   []
								for ex_index, (input_ids, example) in enumerate(zip(_SCREAMING_SNAKE_CASE			,       self.examples    )    ):
									if ex_index % 1_0000 == 0:
										logger.info(f"""Writing example {ex_index}/{len(self.examples    )}"""    )
									# 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(_SCREAMING_SNAKE_CASE    )

									# Zero-pad up to the sequence length.
									a_     			=   batch_length - len(_SCREAMING_SNAKE_CASE    )
									if pad_on_left:
										a_     			=   ([pad_token] * padding_length) + input_ids
										a_     			=   ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
									else:
										a_     			=   input_ids + ([pad_token] * padding_length)
										a_     			=   attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

									if len(_SCREAMING_SNAKE_CASE    ) != batch_length:
										raise ValueError(f"""Error with input length {len(_SCREAMING_SNAKE_CASE    )} vs {batch_length}"""    )
									if len(_SCREAMING_SNAKE_CASE    ) != batch_length:
										raise ValueError(f"""Error with input length {len(_SCREAMING_SNAKE_CASE    )} vs {batch_length}"""    )

									if self.mode == "classification":
										a_     			=   label_map[example.label]
									elif self.mode == "regression":
										a_     			=   float(example.label    )
									else:
										raise ValueError(self.mode    )

									if ex_index < 5 and self.verbose:
										logger.info("""*** Example ***"""    )
										logger.info(f"""guid: {example.guid}"""    )
										logger.info(f"""input_ids: {" ".join([str(_SCREAMING_SNAKE_CASE    ) for x in input_ids]    )}"""    )
										logger.info(f"""attention_mask: {" ".join([str(_SCREAMING_SNAKE_CASE    ) for x in attention_mask]    )}"""    )
										logger.info(f"""label: {example.label} (id = {label})"""    )

									features.append(InputFeatures(input_ids=_SCREAMING_SNAKE_CASE			,       attention_mask=_SCREAMING_SNAKE_CASE			,       label=_SCREAMING_SNAKE_CASE    )    )

								if return_tensors is None:
									return features
								elif return_tensors == "tf":
									if not is_tf_available():
										raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"""    )
									import tensorflow as tf

									def gen():
										for ex in features:
											yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

									a_     			=   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([]    ))			,       )
									return dataset
								elif return_tensors == "pt":
									if not is_torch_available():
										raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported"""    )
									import torch
									from torch.utils.data import TensorDataset

									a_     			=   torch.tensor([f.input_ids for f in features]			,       dtype=torch.long    )
									a_     			=   torch.tensor([f.attention_mask for f in features]			,       dtype=torch.long    )
									if self.mode == "classification":
										a_     			=   torch.tensor([f.label for f in features]			,       dtype=torch.long    )
									elif self.mode == "regression":
										a_     			=   torch.tensor([f.label for f in features]			,       dtype=torch.float    )

									a_     			=   TensorDataset(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    )
									return dataset
								else:
									raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""    )



class 			lowerCamelCase_					:





							def __init__(							self    ):
								a_     			=   [0, 1]





							def   __magic_name__      (							self			,       _SCREAMING_SNAKE_CASE    ):
								if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(_SCREAMING_SNAKE_CASE    ):
										self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
								return self.sequence[:index]







def 			__SCREAMING_SNAKE_CASE		(		)     ->	None:



	"""simple docstring"""






	print(
	    """Fibonacci Series Using Dynamic Programming\n"""     ,					"""Enter the index of the Fibonacci number you want to calculate """     ,					"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""     ,					sep=""""""     ,					)
	a_     			=   Fibonacci()

	while True:
		a_     			=   input(""">> """	)
		if prompt in {"exit", "quit"}:
			break

		try:
			a_     			=   int(UpperCamelCase	)
		except ValueError:
			print("""Enter a number or 'exit'"""	)
			continue

		print(fibonacci.get(UpperCamelCase	)	)


if __name__ == "__main__":
			main()