

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


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)


@dataclass
class     lowerCamelCase_   :
 _lowerCAmelCase       		=  42
 _lowerCAmelCase       		=  42
 _lowerCAmelCase       		=  None
 _lowerCAmelCase       		=  None




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



@dataclass(frozen=_lowerCAmelCase)
class     lowerCamelCase_   :
 _lowerCAmelCase       		=  42
 _lowerCAmelCase       		=  None
 _lowerCAmelCase       		=  None
 _lowerCAmelCase       		=  None




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



class     lowerCamelCase_   :




 def 						a					( self     ,     _UpperCAmelCase       ):
   raise NotImplementedError()




 def 						a					( self     ,     _UpperCAmelCase       ):
   raise NotImplementedError()




 def 						a					( self     ,     _UpperCAmelCase       ):
   raise NotImplementedError()




 def 						a					( self     ,     _UpperCAmelCase       ):
   raise NotImplementedError()




 def 						a					( self       ):
   raise NotImplementedError()




 def 						a					( self     ,     _UpperCAmelCase       ):
   if len(self.get_labels()       ) > 1:
     __UpperCAmelCase  :     List[str]		=    self.get_labels()[int(example.label       )]
   return example




 @classmethod
 def 						a					( cls     ,     _UpperCAmelCase     ,     _UpperCAmelCase=None       ):
   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     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase="classification"     ,     _UpperCAmelCase=False       ):
   __UpperCAmelCase  :     str		=    [] if labels is None else labels
   __UpperCAmelCase  :     List[Any]		=    [] if examples is None else examples
   __UpperCAmelCase  :     Optional[int]		=    mode
   __UpperCAmelCase  :     Optional[int]		=    verbose




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




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




 @classmethod
 def 						a					( cls     ,     _UpperCAmelCase     ,     _UpperCAmelCase=""     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=False     ,     **_UpperCAmelCase       ):
   __UpperCAmelCase  :     Any		=    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 						a					( cls     ,     _UpperCAmelCase     ,     _UpperCAmelCase=None     ,     **_UpperCAmelCase       ):
   __UpperCAmelCase  :     int		=    cls(**_UpperCAmelCase       )
   processor.add_examples(_UpperCAmelCase     ,     labels=_UpperCAmelCase       )
   return processor




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase=""     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=False     ,     ):
   __UpperCAmelCase  :     Optional[Any]		=    self._read_tsv(_UpperCAmelCase       )
   if skip_first_row:
     __UpperCAmelCase  :     Union[str, Any]		=    lines[1:]
   __UpperCAmelCase  :     int		=    []
   __UpperCAmelCase  :     Any		=    []
   __UpperCAmelCase  :     List[Any]		=    []
   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:
       __UpperCAmelCase  :     str		=    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 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=False       ):
   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:
     __UpperCAmelCase  :     List[str]		=    [None] * len(_UpperCAmelCase       )
   if labels is None:
     __UpperCAmelCase  :     Tuple		=    [None] * len(_UpperCAmelCase       )
   __UpperCAmelCase  :     str		=    []
   __UpperCAmelCase  :     Dict		=    set()
   for text_or_text_and_label, label, guid in zip(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
     if isinstance(_UpperCAmelCase     ,     (tuple, list)       ) and label is None:
       __UpperCAmelCase, __UpperCAmelCase  :     Dict		=    text_or_text_and_label
     else:
       __UpperCAmelCase  :     Any		=    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:
     __UpperCAmelCase  :     List[str]		=    examples
   else:
     self.examples.extend(_UpperCAmelCase       )

   # Update labels
   if overwrite_labels:
     __UpperCAmelCase  :     Any		=    list(_UpperCAmelCase       )
   else:
     __UpperCAmelCase  :     Optional[int]		=    list(set(self.labels       ).union(_UpperCAmelCase       )       )

   return self.examples




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=None     ,     ):
   if max_length is None:
     __UpperCAmelCase  :     List[str]		=    tokenizer.max_len

   __UpperCAmelCase  :     Optional[int]		=    {label: i for i, label in enumerate(self.labels       )}

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

     __UpperCAmelCase  :     List[str]		=    tokenizer.encode(
         example.text_a     ,     add_special_tokens=_UpperCAmelCase     ,     max_length=min(_UpperCAmelCase     ,     tokenizer.max_len       )     ,     )
     all_input_ids.append(_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    max(len(_UpperCAmelCase       ) for input_ids in all_input_ids       )

   __UpperCAmelCase  :     str		=    []
   for ex_index, (input_ids, example) in enumerate(zip(_UpperCAmelCase     ,     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.
     __UpperCAmelCase  :     Optional[Any]		=    [1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase       )

     # Zero-pad up to the sequence length.
     __UpperCAmelCase  :     str		=    batch_length - len(_UpperCAmelCase       )
     if pad_on_left:
       __UpperCAmelCase  :     Dict		=    ([pad_token] * padding_length) + input_ids
       __UpperCAmelCase  :     str		=    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
     else:
       __UpperCAmelCase  :     Union[str, Any]		=    input_ids + ([pad_token] * padding_length)
       __UpperCAmelCase  :     Tuple		=    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":
       __UpperCAmelCase  :     Union[str, Any]		=    label_map[example.label]
     elif self.mode == "regression":
       __UpperCAmelCase  :     List[str]		=    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)

     __UpperCAmelCase  :     List[Any]		=    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

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

     __UpperCAmelCase  :     Dict		=    TensorDataset(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )
     return dataset
   else:
     raise ValueError('return_tensors should be one of \'tf\' or \'pt\''       )








def    _snake_case		(snake_case_				:    list						,					snake_case_				:    list     )       ->		list:
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    len(snake_case_     )
  __UpperCAmelCase  :     int		=    (
      first_input_list_length
      if first_input_list_length > second_input_list_length
      else second_input_list_length
  )
  __UpperCAmelCase  :     list		=    []
  for char_count in range(snake_case_     ):
    if char_count < first_input_list_length:
      output_result_list.append(first_input_list[char_count]     )
    if char_count < second_input_list_length:
      output_result_list.append(second_input_list[char_count]     )

  return output_result_list


if __name__ == "__main__":
      print(alternative_list_arrange(['''A''', '''B''', '''C'''], [1, 2, 3, 4, 5]), end=''' ''')





