



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  				=					logging.get_logger(__name__)



@dataclass
class 		_snake_case      :





 """simple docstring"""



 lowerCamelCase_          =			42
 lowerCamelCase_          =			42
 lowerCamelCase_          =			None
 lowerCamelCase_          =			None
 def lowercase_		(						self					)	->							Any:




   """simple docstring"""







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







@dataclass(frozen=lowerCamelCase				)
class 		_snake_case      :





 """simple docstring"""



 lowerCamelCase_          =			42
 lowerCamelCase_          =			None
 lowerCamelCase_          =			None
 lowerCamelCase_          =			None
 def lowercase_		(						self					)	->							Any:




   """simple docstring"""







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







class 		_snake_case      :





 """simple docstring"""



 def lowercase_		(						self	,    a					)	->							Optional[Any]:




   """simple docstring"""







   raise NotImplementedError()
 def lowercase_		(						self	,    a					)	->							int:




   """simple docstring"""







   raise NotImplementedError()
 def lowercase_		(						self	,    a					)	->							str:




   """simple docstring"""







   raise NotImplementedError()
 def lowercase_		(						self	,    a					)	->							str:




   """simple docstring"""







   raise NotImplementedError()
 def lowercase_		(						self					)	->							Optional[int]:




   """simple docstring"""







   raise NotImplementedError()
 def lowercase_		(						self	,    a					)	->							Any:




   """simple docstring"""







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



 @classmethod
 def lowercase_		(						cls	,    a	,    a=None					)	->							Union[str, Any]:




   """simple docstring"""







   with open(a	,    '''r'''	,    encoding='''utf-8-sig'''					) as f:
     return list(csv.reader(a	,    delimiter='''\t'''	,    quotechar=a					)					)







class 		_snake_case      (			lowerCamelCase				):





 """simple docstring"""



 def __init__(						self	,    a=None	,    a=None	,    a="classification"	,    a=False					)	->							str:




   """simple docstring"""







   _A           =    [] if labels is None else labels
   _A           =    [] if examples is None else examples
   _A           =    mode
   _A           =    verbose
 def __len__(						self					)	->							Tuple:




   """simple docstring"""







   return len(self.examples					)
 def __getitem__(						self	,    a					)	->							Tuple:




   """simple docstring"""







   if isinstance(a	,    a					):
     return SingleSentenceClassificationProcessor(labels=self.labels	,    examples=self.examples[idx]					)
   return self.examples[idx]
 @classmethod
 def lowercase_		(						cls	,    a	,    a=""	,    a=0	,    a=1	,    a=None	,    a=False	,    **a					)	->							List[Any]:




   """simple docstring"""







   _A           =    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 lowercase_		(						cls	,    a	,    a=None	,    **a					)	->							str:




   """simple docstring"""







   _A           =    cls(**a					)
   processor.add_examples(a	,    labels=a					)
   return processor
 def lowercase_		(						self	,    a	,    a=""	,    a=0	,    a=1	,    a=None	,    a=False	,    a=False	,    a=False	,    )	->							Any:




   """simple docstring"""







   _A           =    self._read_tsv(a					)
   if skip_first_row:
     _A           =    lines[1:]
   _A           =    []
   _A           =    []
   _A           =    []
   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           =    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 lowercase_		(						self	,    a	,    a=None	,    a=None	,    a=False	,    a=False					)	->							Optional[Any]:




   """simple docstring"""







   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           =    [None] * len(a					)
   if labels is None:
     _A           =    [None] * len(a					)
   _A           =    []
   _A           =    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           =    text_or_text_and_label
     else:
       _A           =    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           =    examples
   else:
     self.examples.extend(a					)

   # Update labels
   if overwrite_labels:
     _A           =    list(a					)
   else:
     _A           =    list(set(self.labels					).union(a					)					)

   return self.examples



 def lowercase_		(						self	,    a	,    a=None	,    a=False	,    a=0	,    a=True	,    a=None	,    )	->							Dict:




   """simple docstring"""







   if max_length is None:
     _A           =    tokenizer.max_len

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

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

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

   _A           =    max(len(a					) for input_ids in all_input_ids					)

   _A           =    []
   for ex_index, (input_ids, example) in enumerate(zip(a	,    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.
     _A           =    [1 if mask_padding_with_zero else 0] * len(a					)

     # Zero-pad up to the sequence length.
     _A           =    batch_length - len(a					)
     if pad_on_left:
       _A           =    ([pad_token] * padding_length) + input_ids
       _A           =    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
     else:
       _A           =    input_ids + ([pad_token] * padding_length)
       _A           =    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           =    label_map[example.label]
     elif self.mode == "regression":
       _A           =    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           =    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           =    torch.tensor([f.input_ids for f in features]	,    dtype=torch.long					)
     _A           =    torch.tensor([f.attention_mask for f in features]	,    dtype=torch.long					)
     if self.mode == "classification":
       _A           =    torch.tensor([f.label for f in features]	,    dtype=torch.long					)
     elif self.mode == "regression":
       _A           =    torch.tensor([f.label for f in features]	,    dtype=torch.float					)

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




from ..utils import requires_backends


__lowerCamelCase  				=					None



class 		_snake_case      :





 """simple docstring"""



 def __init__(						self	,    *a	,    **a					)	->							Tuple:




   """simple docstring"""







   requires_backends(self	,    ['''detectron2''']					)



 @classmethod
 def lowercase_		(						cls	,    *a	,    **a					)	->							Optional[int]:




   """simple docstring"""







   requires_backends(cls	,    ['''detectron2''']					)