

"""simple docstring"""




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_							:     Any     =	logging.get_logger(__name__)





@dataclass
class 				lowerCamelCase  :
   lowerCamelCase__				:						str   						=							4_2
   lowerCamelCase__				:						str   						=							4_2
   lowerCamelCase__				:						Optional[Any]   						=							None
   lowerCamelCase__				:						int   						=							None

   def    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      int:
      return json.dumps(dataclasses.asdict(self    )      ,			indent=2    ) + "\n"





@dataclass(frozen=_lowerCamelCase     )
class 				lowerCamelCase  :
   lowerCamelCase__				:						Optional[int]   						=							4_2
   lowerCamelCase__				:						Union[str, Any]   						=							None
   lowerCamelCase__				:						int   						=							None
   lowerCamelCase__				:						Dict   						=							None

   def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Optional[int]:
      return json.dumps(dataclasses.asdict(self    )    ) + "\n"





class 				lowerCamelCase  :

   def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  List[Any]    )				->      Tuple:
      raise NotImplementedError()

   def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  List[str]    )				->      Any:
      raise NotImplementedError()

   def    SCREAMING_SNAKE_CASE			(   self			:  Any      ,			__UpperCAmelCase			:  Any    )				->      Any:
      raise NotImplementedError()

   def    SCREAMING_SNAKE_CASE			(   self			:  Optional[Any]      ,			__UpperCAmelCase			:  Tuple    )				->      int:
      raise NotImplementedError()

   def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Optional[int]:
      raise NotImplementedError()

   def    SCREAMING_SNAKE_CASE			(   self			:  str      ,			__UpperCAmelCase			:  Dict    )				->      Any:







      if len(self.get_labels()    ) > 1:
         SCREAMING_SNAKE_CASE__									= self.get_labels()[int(example.label    )]
      return example





   @classmethod
   def    SCREAMING_SNAKE_CASE			(   cls			:  int      ,			__UpperCAmelCase			:  str      ,			__UpperCAmelCase			:  Union[str, Any]=None    )				->      Any:







      with open(__UpperCamelCase      ,			"""r"""      ,			encoding="""utf-8-sig"""    ) as f:
         return list(csv.reader(__UpperCamelCase      ,			delimiter="""\t"""      ,			quotechar=__UpperCamelCase    )    )





class 				lowerCamelCase  (_lowerCamelCase     ):

   def __init__(   self			:  Union[str, Any]      ,			__UpperCAmelCase			:  Any=None      ,			__UpperCAmelCase			:  int=None      ,			__UpperCAmelCase			:  List[Any]="classification"      ,			__UpperCAmelCase			:  Union[str, Any]=False    )				->      Union[str, Any]:
      SCREAMING_SNAKE_CASE__									= [] if labels is None else labels
      SCREAMING_SNAKE_CASE__									= [] if examples is None else examples
      SCREAMING_SNAKE_CASE__									= mode
      SCREAMING_SNAKE_CASE__									= verbose

   def __len__(   self			:  Optional[Any]    )				->      Dict:
      return len(self.examples    )

   def __getitem__(   self			:  str      ,			__UpperCAmelCase			:  int    )				->      List[str]:







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

   @classmethod
   def    SCREAMING_SNAKE_CASE			(   cls			:  Dict      ,			__UpperCAmelCase			:  List[Any]      ,			__UpperCAmelCase			:  Optional[Any]=""      ,			__UpperCAmelCase			:  Union[str, Any]=0      ,			__UpperCAmelCase			:  Dict=1      ,			__UpperCAmelCase			:  Union[str, Any]=None      ,			__UpperCAmelCase			:  str=False      ,			**__UpperCAmelCase			:  Dict    )				->      Optional[Any]:
      SCREAMING_SNAKE_CASE__									= cls(**__UpperCamelCase    )
      processor.add_examples_from_csv(
          __UpperCamelCase      ,			split_name=__UpperCamelCase      ,			column_label=__UpperCamelCase      ,			column_text=__UpperCamelCase      ,			column_id=__UpperCamelCase      ,			skip_first_row=__UpperCamelCase      ,			overwrite_labels=__UpperCamelCase      ,			overwrite_examples=__UpperCamelCase      ,			)
      return processor

   @classmethod
   def    SCREAMING_SNAKE_CASE			(   cls			:  Union[str, Any]      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  Optional[int]=None      ,			**__UpperCAmelCase			:  Dict    )				->      Optional[int]:
      SCREAMING_SNAKE_CASE__									= cls(**__UpperCamelCase    )
      processor.add_examples(__UpperCamelCase      ,			labels=__UpperCamelCase    )
      return processor

   def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Optional[Any]=""      ,			__UpperCAmelCase			:  Union[str, Any]=0      ,			__UpperCAmelCase			:  List[Any]=1      ,			__UpperCAmelCase			:  Optional[Any]=None      ,			__UpperCAmelCase			:  int=False      ,			__UpperCAmelCase			:  Union[str, Any]=False      ,			__UpperCAmelCase			:  List[Any]=False      ,			)				->      List[Any]:
      SCREAMING_SNAKE_CASE__									= self._read_tsv(__UpperCamelCase    )
      if skip_first_row:
         SCREAMING_SNAKE_CASE__									= lines[1:]
      SCREAMING_SNAKE_CASE__									= []
      SCREAMING_SNAKE_CASE__									= []
      SCREAMING_SNAKE_CASE__									= []
      for i, line in enumerate(__UpperCamelCase    ):
         texts.append(line[column_text]    )
         labels.append(line[column_label]    )
         if column_id is not None:
            ids.append(line[column_id]    )
         else:
            SCREAMING_SNAKE_CASE__									= F"""{split_name}-{i}""" if split_name else str(__UpperCamelCase    )
            ids.append(__UpperCamelCase    )

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

   def    SCREAMING_SNAKE_CASE			(   self			:  Tuple      ,			__UpperCAmelCase			:  List[str]      ,			__UpperCAmelCase			:  List[Any]=None      ,			__UpperCAmelCase			:  str=None      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Tuple=False    )				->      List[str]:







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

      # Update examples
      if overwrite_examples:
         SCREAMING_SNAKE_CASE__									= examples
      else:
         self.examples.extend(__UpperCamelCase    )

      # Update labels
      if overwrite_labels:
         SCREAMING_SNAKE_CASE__									= list(__UpperCamelCase    )
      else:
         SCREAMING_SNAKE_CASE__									= list(set(self.labels    ).union(__UpperCamelCase    )    )

      return self.examples





   def    SCREAMING_SNAKE_CASE			(   self			:  int      ,			__UpperCAmelCase			:  Union[str, Any]      ,			__UpperCAmelCase			:  Optional[int]=None      ,			__UpperCAmelCase			:  List[Any]=False      ,			__UpperCAmelCase			:  Any=0      ,			__UpperCAmelCase			:  Dict=True      ,			__UpperCAmelCase			:  int=None      ,			)				->      Dict:







      if max_length is None:
         SCREAMING_SNAKE_CASE__									= tokenizer.max_len

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

      SCREAMING_SNAKE_CASE__									= []
      for ex_index, example in enumerate(self.examples    ):
         if ex_index % 1_0_0_0_0 == 0:
            logger.info(F"""Tokenizing example {ex_index}"""    )

         SCREAMING_SNAKE_CASE__									= tokenizer.encode(
             example.text_a      ,			add_special_tokens=__UpperCamelCase      ,			max_length=min(__UpperCamelCase      ,			tokenizer.max_len    )      ,			)
         all_input_ids.append(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE__									= max(len(__UpperCamelCase    ) for input_ids in all_input_ids    )

      SCREAMING_SNAKE_CASE__									= []
      for ex_index, (input_ids, example) in enumerate(zip(__UpperCamelCase      ,			self.examples    )    ):
         if ex_index % 1_0_0_0_0 == 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.
         SCREAMING_SNAKE_CASE__									= [1 if mask_padding_with_zero else 0] * len(__UpperCamelCase    )

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

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

         if self.mode == "classification":
            SCREAMING_SNAKE_CASE__									= label_map[example.label]
         elif self.mode == "regression":
            SCREAMING_SNAKE_CASE__									= 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(__UpperCamelCase    ) for x in input_ids]    )}"""    )
            logger.info(F"""attention_mask: {" ".join([str(__UpperCamelCase    ) for x in attention_mask]    )}"""    )
            logger.info(F"""label: {example.label} (id = {label})"""    )

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

      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)

         SCREAMING_SNAKE_CASE__									= tf.data.Dataset.from_generator(
             __UpperCamelCase      ,			({"""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

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

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





"""simple docstring"""




class 				lowerCamelCase  :

			def __init__(   self			:  Dict    )				->      None:
						SCREAMING_SNAKE_CASE__									= [0, 1]





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  int    )				->      list:
						if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(__UpperCAmelCase    ):
												self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
						return self.sequence[:index]

def 		A    (       ):




			'''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=""""""						,			)
			SCREAMING_SNAKE_CASE__									= Fibonacci()

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

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

						print(fibonacci.get(snake_case__      )      )


if __name__ == "__main__":
						main()


