






'''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


SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)




@dataclass
class 							lowerCAmelCase					:







   """simple docstring"""







   _A						=   42
   _A						=   42
   _A						=   None
   _A						=   None



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




@dataclass(frozen=lowerCamelCase__   )
class 							lowerCAmelCase					:







   """simple docstring"""







   _A						=   42
   _A						=   None
   _A						=   None
   _A						=   None



   def    __magic_name__    ( self			)    ->   List[str]:
    return json.dumps(dataclasses.asdict(self			)			) + "\n"




class 							lowerCAmelCase					:







   """simple docstring"""







   def    __magic_name__    ( self   ,   _A			)    ->   Dict:
    raise NotImplementedError()



   def    __magic_name__    ( self   ,   _A			)    ->   Union[str, Any]:
    raise NotImplementedError()



   def    __magic_name__    ( self   ,   _A			)    ->   List[str]:
    raise NotImplementedError()



   def    __magic_name__    ( self   ,   _A			)    ->   List[str]:
    raise NotImplementedError()



   def    __magic_name__    ( self			)    ->   Any:
    raise NotImplementedError()



   def    __magic_name__    ( self   ,   _A			)    ->   Dict:
    if len(self.get_labels()			) > 1:
     __a    :	str									=						self.get_labels()[int(example.label			)]
    return example



   @classmethod
   def    __magic_name__    ( cls   ,   _A   ,   _A=None			)    ->   Dict:
    with open(_A   ,   'r'   ,   encoding='utf-8-sig'			) as f:
     return list(csv.reader(_A   ,   delimiter='\t'   ,   quotechar=_A			)			)




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   def __init__( self   ,   _A=None   ,   _A=None   ,   _A="classification"   ,   _A=False			)    ->   Union[str, Any]:
    __a    :	Union[str, Any]									=						[] if labels is None else labels
    __a    :	Tuple									=						[] if examples is None else examples
    __a    :	Optional[Any]									=						mode
    __a    :	Union[str, Any]									=						verbose



   def __len__( self			)    ->   Any:
    return len(self.examples			)



   def __getitem__( self   ,   _A			)    ->   List[Any]:
    if isinstance(_A   ,   _A			):
     return SingleSentenceClassificationProcessor(labels=self.labels   ,   examples=self.examples[idx]			)
    return self.examples[idx]



   @classmethod
   def    __magic_name__    ( cls   ,   _A   ,   _A=""   ,   _A=0   ,   _A=1   ,   _A=None   ,   _A=False   ,   **_A			)    ->   Tuple:
    __a    :	List[str]									=						cls(**_A			)
    processor.add_examples_from_csv(
        _A   ,   split_name=_A   ,   column_label=_A   ,   column_text=_A   ,   column_id=_A   ,   skip_first_row=_A   ,   overwrite_labels=_A   ,   overwrite_examples=_A   ,   )
    return processor



   @classmethod
   def    __magic_name__    ( cls   ,   _A   ,   _A=None   ,   **_A			)    ->   Any:
    __a    :	List[str]									=						cls(**_A			)
    processor.add_examples(_A   ,   labels=_A			)
    return processor



   def    __magic_name__    ( self   ,   _A   ,   _A=""   ,   _A=0   ,   _A=1   ,   _A=None   ,   _A=False   ,   _A=False   ,   _A=False   ,   )    ->   List[str]:
    __a    :	Optional[int]									=						self._read_tsv(_A			)
    if skip_first_row:
     __a    :	Optional[Any]									=						lines[1:]
    __a    :	Tuple									=						[]
    __a    :	Optional[Any]									=						[]
    __a    :	str									=						[]
    for i, line in enumerate(_A			):
     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(_A			)
      ids.append(_A			)

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



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

    # Update examples
    if overwrite_examples:
     __a    :	Optional[Any]									=						examples
    else:
     self.examples.extend(_A			)

    # Update labels
    if overwrite_labels:
     __a    :	List[Any]									=						list(_A			)
    else:
     __a    :	Union[str, Any]									=						list(set(self.labels			).union(_A			)			)

    return self.examples



   def    __magic_name__    ( self   ,   _A   ,   _A=None   ,   _A=False   ,   _A=0   ,   _A=True   ,   _A=None   ,   )    ->   str:
    if max_length is None:
     __a    :	int									=						tokenizer.max_len

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

    __a    :	str									=						[]
    for ex_index, example in enumerate(self.examples			):
     if ex_index % 10000 == 0:
      logger.info(f'''Tokenizing example {ex_index}'''			)

     __a    :	Tuple									=						tokenizer.encode(
         example.text_a   ,   add_special_tokens=_A   ,   max_length=min(_A   ,   tokenizer.max_len			)   ,   )
     all_input_ids.append(_A			)

    __a    :	List[Any]									=						max(len(_A			) for input_ids in all_input_ids			)

    __a    :	Optional[int]									=						[]
    for ex_index, (input_ids, example) in enumerate(zip(_A   ,   self.examples			)			):
     if ex_index % 10000 == 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    :	Any									=						[1 if mask_padding_with_zero else 0] * len(_A			)

     # Zero-pad up to the sequence length.
     __a    :	Optional[Any]									=						batch_length - len(_A			)
     if pad_on_left:
      __a    :	int									=						([pad_token] * padding_length) + input_ids
      __a    :	Optional[Any]									=						([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
     else:
      __a    :	List[Any]									=						input_ids + ([pad_token] * padding_length)
      __a    :	List[Any]									=						attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

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

     if self.mode == "classification":
      __a    :	Dict									=						label_map[example.label]
     elif self.mode == "regression":
      __a    :	Tuple									=						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(_A			) for x in input_ids]			)}'''			)
      logger.info(f'''attention_mask: {' '.join([str(_A			) for x in attention_mask]			)}'''			)
      logger.info(f'''label: {example.label} (id = {label})'''			)

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

    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[Any]									=						tf.data.Dataset.from_generator(
         _A   ,   ({'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    :	int									=						torch.tensor([f.input_ids for f in features]   ,   dtype=torch.long			)
     __a    :	str									=						torch.tensor([f.attention_mask for f in features]   ,   dtype=torch.long			)
     if self.mode == "classification":
      __a    :	str									=						torch.tensor([f.label for f in features]   ,   dtype=torch.long			)
     elif self.mode == "regression":
      __a    :	Optional[Any]									=						torch.tensor([f.label for f in features]   ,   dtype=torch.float			)

     __a    :	Optional[Any]									=						TensorDataset(_A   ,   _A   ,   _A			)
     return dataset
    else:
     raise ValueError('return_tensors should be one of \'tf\' or \'pt\''			)












'''simple docstring'''


def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if len(SCREAMING_SNAKE_CASE__		) <= 1:
  return data
 else:
  return [
      *quick_sort([e for e in data[1:] if e <= data[0]]		),
      data[0],
      *quick_sort([e for e in data[1:] if e > data[0]]		),
  ]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




