





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


_lowerCAmelCase							:      Tuple						=	logging.get_logger(__name__)


@dataclass
class 					__magic_name__  :





     """simple docstring"""



     __UpperCamelCase					    =     42
     __UpperCamelCase					    =     42
     __UpperCamelCase					    =     None
     __UpperCamelCase					    =     None



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple  ):





        '''simple docstring'''




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




@dataclass(frozen=lowerCamelCase__							)
class 					__magic_name__  :





     """simple docstring"""



     __UpperCamelCase					    =     42
     __UpperCamelCase					    =     None
     __UpperCamelCase					    =     None
     __UpperCamelCase					    =     None



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]  ):





        '''simple docstring'''




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




class 					__magic_name__  :





     """simple docstring"""



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        raise NotImplementedError()



     def 						SCREAMING_SNAKE_CASE		(							self       :Any			,      snake_case       :Optional[int]  ):





        '''simple docstring'''




        raise NotImplementedError()



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :Dict  ):





        '''simple docstring'''




        raise NotImplementedError()



     def 						SCREAMING_SNAKE_CASE		(							self       :Any			,      snake_case       :Tuple  ):





        '''simple docstring'''




        raise NotImplementedError()



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple  ):





        '''simple docstring'''




        raise NotImplementedError()



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :Tuple  ):





        '''simple docstring'''




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





     @classmethod
     def 						SCREAMING_SNAKE_CASE		(							cls       :List[Any]			,      snake_case       :Tuple			,      snake_case       :Optional[int]=None  ):





        '''simple docstring'''




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




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     def __init__(							self       :Optional[int]			,      snake_case       :int=None			,      snake_case       :List[str]=None			,      snake_case       :Optional[int]="classification"			,      snake_case       :Optional[Any]=False  ):





        '''simple docstring'''




        A_					:				int          =			[] if labels is None else labels
        A_					:				List[Any]          =			[] if examples is None else examples
        A_					:				Optional[int]          =			mode
        A_					:				str          =			verbose



     def __len__(							self       :List[Any]  ):





        '''simple docstring'''




        return len(self.examples  )



     def __getitem__(							self       :Any			,      snake_case       :Tuple  ):





        '''simple docstring'''




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



     @classmethod
     def 						SCREAMING_SNAKE_CASE		(							cls       :Optional[int]			,      snake_case       :Tuple			,      snake_case       :Any=""			,      snake_case       :Any=0			,      snake_case       :Optional[int]=1			,      snake_case       :List[Any]=None			,      snake_case       :Optional[int]=False			,      **snake_case       :List[Any]  ):





        '''simple docstring'''




        A_					:				int          =			cls(**snake_case  )
        processor.add_examples_from_csv(
            snake_case			,      split_name=snake_case			,      column_label=snake_case			,      column_text=snake_case			,      column_id=snake_case			,      skip_first_row=snake_case			,      overwrite_labels=snake_case			,      overwrite_examples=snake_case			,      )
        return processor



     @classmethod
     def 						SCREAMING_SNAKE_CASE		(							cls       :Dict			,      snake_case       :List[Any]			,      snake_case       :List[Any]=None			,      **snake_case       :str  ):





        '''simple docstring'''




        A_					:				int          =			cls(**snake_case  )
        processor.add_examples(snake_case			,      labels=snake_case  )
        return processor



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]			,      snake_case       :Optional[int]			,      snake_case       :Optional[int]=""			,      snake_case       :str=0			,      snake_case       :List[str]=1			,      snake_case       :Union[str, Any]=None			,      snake_case       :Dict=False			,      snake_case       :List[Any]=False			,      snake_case       :str=False			,      ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			self._read_tsv(snake_case  )
        if skip_first_row:
           A_					:				int          =			lines[1:]
        A_					:				Optional[int]          =			[]
        A_					:				int          =			[]
        A_					:				List[Any]          =			[]
        for i, line in enumerate(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_					:				Any          =			f"{split_name}-{i}" if split_name else str(snake_case  )
              ids.append(snake_case  )

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



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :Optional[Any]			,      snake_case       :Tuple=None			,      snake_case       :Optional[Any]=None			,      snake_case       :int=False			,      snake_case       :Optional[int]=False  ):





        '''simple docstring'''




        if labels is not None and len(snake_case  ) != len(snake_case  ):
           raise ValueError(
               f"Text and labels have mismatched lengths {len(snake_case  )} and {len(snake_case  )}"  )
        if ids is not None and len(snake_case  ) != len(snake_case  ):
           raise ValueError(f"Text and ids have mismatched lengths {len(snake_case  )} and {len(snake_case  )}"  )
        if ids is None:
           A_					:				int          =			[None] * len(snake_case  )
        if labels is None:
           A_					:				List[str]          =			[None] * len(snake_case  )
        A_					:				Optional[Any]          =			[]
        A_					:				List[Any]          =			set()
        for text_or_text_and_label, label, guid in zip(snake_case			,      snake_case			,      snake_case  ):
           if isinstance(snake_case			,      (tuple, list)  ) and label is None:
              A_      ,						A_					:				Dict          =			text_or_text_and_label
           else:
              A_					:				Any          =			text_or_text_and_label
           added_labels.add(snake_case  )
           examples.append(InputExample(guid=snake_case			,      text_a=snake_case			,      text_b=snake_case			,      label=snake_case  )  )

        # Update examples
        if overwrite_examples:
           A_					:				Any          =			examples
        else:
           self.examples.extend(snake_case  )

        # Update labels
        if overwrite_labels:
           A_					:				List[str]          =			list(snake_case  )
        else:
           A_					:				Tuple          =			list(set(self.labels  ).union(snake_case  )  )

        return self.examples





     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :Any			,      snake_case       :Union[str, Any]=None			,      snake_case       :Dict=False			,      snake_case       :Dict=0			,      snake_case       :Union[str, Any]=True			,      snake_case       :Any=None			,      ):





        '''simple docstring'''




        if max_length is None:
           A_					:				Union[str, Any]          =			tokenizer.max_len

        A_					:				Union[str, Any]          =			{label: i for i, label in enumerate(self.labels  )}

        A_					:				Tuple          =			[]
        for ex_index, example in enumerate(self.examples  ):
           if ex_index % 10_000 == 0:
              logger.info(f"Tokenizing example {ex_index}"  )

           A_					:				Optional[int]          =			tokenizer.encode(
               example.text_a			,      add_special_tokens=snake_case			,      max_length=min(snake_case			,      tokenizer.max_len  )			,      )
           all_input_ids.append(snake_case  )

        A_					:				Any          =			max(len(snake_case  ) for input_ids in all_input_ids  )

        A_					:				Any          =			[]
        for ex_index, (input_ids, example) in enumerate(zip(snake_case			,      self.examples  )  ):
           if ex_index % 10_000 == 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_					:				Optional[Any]          =			[1 if mask_padding_with_zero else 0] * len(snake_case  )

           # Zero-pad up to the sequence length.
           A_					:				List[str]          =			batch_length - len(snake_case  )
           if pad_on_left:
              A_					:				Union[str, Any]          =			([pad_token] * padding_length) + input_ids
              A_					:				List[Any]          =			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
           else:
              A_					:				List[str]          =			input_ids + ([pad_token] * padding_length)
              A_					:				Tuple          =			attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

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

           if self.mode == "classification":
              A_					:				List[Any]          =			label_map[example.label]
           elif self.mode == "regression":
              A_					:				Optional[Any]          =			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(snake_case  ) for x in input_ids]  )}"  )
              logger.info(f"attention_mask: {' '.join([str(snake_case  ) for x in attention_mask]  )}"  )
              logger.info(f"label: {example.label} (id = {label})"  )

           features.append(InputFeatures(input_ids=snake_case			,      attention_mask=snake_case			,      label=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_					:				Optional[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([]  ))			,      )
           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_					:				List[str]          =			torch.tensor([f.input_ids for f in features]			,      dtype=torch.long  )
           A_					:				Any          =			torch.tensor([f.attention_mask for f in features]			,      dtype=torch.long  )
           if self.mode == "classification":
              A_					:				Any          =			torch.tensor([f.label for f in features]			,      dtype=torch.long  )
           elif self.mode == "regression":
              A_					:				List[str]          =			torch.tensor([f.label for f in features]			,      dtype=torch.float  )

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







def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : int = 1000	) ->						str:
   if (not isinstance(_lowerCAmelCase				,				_lowerCAmelCase	)) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer"	)
   elif (not isinstance(_lowerCAmelCase				,				_lowerCAmelCase	)) or (precision < 0):
      raise ValueError("Precision must be a nonnegative integer"	)

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   A_					:				List[str]          =			(
       4 * _subsum(_lowerCAmelCase				,				1				,				_lowerCAmelCase	)
       - 2 * _subsum(_lowerCAmelCase				,				4				,				_lowerCAmelCase	)
       - _subsum(_lowerCAmelCase				,				5				,				_lowerCAmelCase	)
       - _subsum(_lowerCAmelCase				,				6				,				_lowerCAmelCase	)
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 16	)	)[2:]

def      __snake_case						(					_lowerCAmelCase : int				,				_lowerCAmelCase : int				,				_lowerCAmelCase : int	) ->						float:
   A_					:				int          =			0.0
   for sum_index in range(digit_pos_to_extract + precision	):
      A_					:				Optional[int]          =			8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         A_					:				Optional[Any]          =			pow(
             16				,				digit_pos_to_extract - 1 - sum_index				,				_lowerCAmelCase	)
      else:
         A_					:				Tuple          =			pow(16				,				digit_pos_to_extract - 1 - sum_index	)
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
 import doctest

 doctest.testmod()
