





"""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__       :  Dict       		=					logging.get_logger(__name__)



@dataclass
class   lowercase_					:
       snake_case__					:				str
       snake_case__					:				str
       snake_case__					:				Optional[str]									=  None
       snake_case__					:				Optional[str]									=  None

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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



@dataclass(frozen=__lowerCAmelCase				)
class   lowercase_					:
       snake_case__					:				List[int]
       snake_case__					:				Optional[List[int]]									=  None
       snake_case__					:				Optional[List[int]]									=  None
       snake_case__					:				Optional[Union[int, float]]									=  None

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

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



class   lowercase_					:

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        raise NotImplementedError()

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        raise NotImplementedError()

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        raise NotImplementedError()

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        raise NotImplementedError()

       def 		_SCREAMING_SNAKE_CASE(      self  ):






        '''simple docstring'''

        raise NotImplementedError()

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

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

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(      cls ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None  ):






        '''simple docstring'''

        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   lowercase_					(  __lowerCAmelCase				):

       def __init__(      self ,       __SCREAMING_SNAKE_CASE=None ,       __SCREAMING_SNAKE_CASE=None ,       __SCREAMING_SNAKE_CASE="classification" ,       __SCREAMING_SNAKE_CASE=False  ):






        '''simple docstring'''

        snake_case__     				=					[] if labels is None else labels
        snake_case__     				=					[] if examples is None else examples
        snake_case__     				=					mode
        snake_case__     				=					verbose

       def __len__(      self  ):






        '''simple docstring'''

        return len(self.examples  )

       def __getitem__(      self ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

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

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(      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  ):






        '''simple docstring'''

        snake_case__     				=					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 		_SCREAMING_SNAKE_CASE(      cls ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None ,       **__SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        snake_case__     				=					cls(**__SCREAMING_SNAKE_CASE  )
        processor.add_examples(__SCREAMING_SNAKE_CASE ,       labels=__SCREAMING_SNAKE_CASE  )
        return processor

       def 		_SCREAMING_SNAKE_CASE(      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 ,       ):






        '''simple docstring'''

        snake_case__     				=					self._read_tsv(__SCREAMING_SNAKE_CASE  )
        if skip_first_row:
         snake_case__     				=					lines[1:]
        snake_case__     				=					[]
        snake_case__     				=					[]
        snake_case__     				=					[]
        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:
          snake_case__     				=					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 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None ,       __SCREAMING_SNAKE_CASE=None ,       __SCREAMING_SNAKE_CASE=False ,       __SCREAMING_SNAKE_CASE=False  ):






        '''simple docstring'''

        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:
         snake_case__     				=					[None] * len(__SCREAMING_SNAKE_CASE  )
        if labels is None:
         snake_case__     				=					[None] * len(__SCREAMING_SNAKE_CASE  )
        snake_case__     				=					[]
        snake_case__     				=					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:
          snake_case__				, snake_case__     				=					text_or_text_and_label
         else:
          snake_case__     				=					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:
         snake_case__     				=					examples
        else:
         self.examples.extend(__SCREAMING_SNAKE_CASE  )

        # Update labels
        if overwrite_labels:
         snake_case__     				=					list(__SCREAMING_SNAKE_CASE  )
        else:
         snake_case__     				=					list(set(self.labels  ).union(__SCREAMING_SNAKE_CASE  )  )

        return self.examples

       def 		_SCREAMING_SNAKE_CASE(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE=None ,       __SCREAMING_SNAKE_CASE=False ,       __SCREAMING_SNAKE_CASE=0 ,       __SCREAMING_SNAKE_CASE=True ,       __SCREAMING_SNAKE_CASE=None ,       ):






        '''simple docstring'''

        if max_length is None:
         snake_case__     				=					tokenizer.max_len

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

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

         snake_case__     				=					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  )

        snake_case__     				=					max(len(__SCREAMING_SNAKE_CASE  ) for input_ids in all_input_ids  )

        snake_case__     				=					[]
        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.
         snake_case__     				=					[1 if mask_padding_with_zero else 0] * len(__SCREAMING_SNAKE_CASE  )

         # Zero-pad up to the sequence length.
         snake_case__     				=					batch_length - len(__SCREAMING_SNAKE_CASE  )
         if pad_on_left:
          snake_case__     				=					([pad_token] * padding_length) + input_ids
          snake_case__     				=					([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
         else:
          snake_case__     				=					input_ids + ([pad_token] * padding_length)
          snake_case__     				=					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":
          snake_case__     				=					label_map[example.label]
         elif self.mode == "regression":
          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(__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)

         snake_case__     				=					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

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

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








"""simple docstring"""


def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						)     ->	float:

 '''simple docstring'''






 snake_case__     				=					[1 / first_term]
 snake_case__     				=					1 / first_term
 for _ in range(number_of_terms - 1						):
  first_term += common_difference
  arithmetic_progression.append(_UpperCAmelCase						)
 snake_case__     				=					[1 / step for step in arithmetic_progression]
 return sum(_UpperCAmelCase						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

