






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 :    Optional[Any]								=				logging.get_logger(__name__)


@dataclass
class  __lowercase :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			42
     SCREAMING_SNAKE_CASE				  =			42
     SCREAMING_SNAKE_CASE				  =			None
     SCREAMING_SNAKE_CASE				  =			None




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



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


@dataclass(frozen=lowerCamelCase__					)
class  __lowercase :







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			42
     SCREAMING_SNAKE_CASE				  =			None
     SCREAMING_SNAKE_CASE				  =			None
     SCREAMING_SNAKE_CASE				  =			None




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



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


class  __lowercase :







     '''simple docstring'''




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		UpperCamelCase_    :     List[str]			):


         """simple docstring"""



         raise NotImplementedError()




     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     Dict			):


         """simple docstring"""



         raise NotImplementedError()




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     int			):


         """simple docstring"""



         raise NotImplementedError()




     def 						lowerCAmelCase_       (			self    :     int	,		UpperCamelCase_    :     Dict			):


         """simple docstring"""



         raise NotImplementedError()




     def 						lowerCAmelCase_       (			self    :     str			):


         """simple docstring"""



         raise NotImplementedError()




     def 						lowerCAmelCase_       (			self    :     Dict	,		UpperCamelCase_    :     str			):


         """simple docstring"""



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





     @classmethod
     def 						lowerCAmelCase_       (			cls    :     Optional[int]	,		UpperCamelCase_    :     List[Any]	,		UpperCamelCase_    :     Union[str, Any]=None			):


         """simple docstring"""



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


class  __lowercase (		lowerCamelCase__					):







     '''simple docstring'''




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


         """simple docstring"""



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




     def __len__(			self    :     int			):


         """simple docstring"""



         return len(self.examples			)




     def __getitem__(			self    :     Union[str, Any]	,		UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



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




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     List[Any]	,		UpperCamelCase_    :     Union[str, Any]	,		UpperCamelCase_    :     Any=""	,		UpperCamelCase_    :     int=0	,		UpperCamelCase_    :     List[Any]=1	,		UpperCamelCase_    :     Optional[int]=None	,		UpperCamelCase_    :     int=False	,		**UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



         __A             =		cls(**lowercase_			)
         processor.add_examples_from_csv(
             lowercase_	,		split_name=lowercase_	,		column_label=lowercase_	,		column_text=lowercase_	,		column_id=lowercase_	,		skip_first_row=lowercase_	,		overwrite_labels=lowercase_	,		overwrite_examples=lowercase_	,		)
         return processor




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     List[Any]	,		UpperCamelCase_    :     Dict	,		UpperCamelCase_    :     Union[str, Any]=None	,		**UpperCamelCase_    :     int			):


         """simple docstring"""



         __A             =		cls(**lowercase_			)
         processor.add_examples(lowercase_	,		labels=lowercase_			)
         return processor




     def 						lowerCAmelCase_       (			self    :     Tuple	,		UpperCamelCase_    :     Any	,		UpperCamelCase_    :     Optional[int]=""	,		UpperCamelCase_    :     str=0	,		UpperCamelCase_    :     Optional[Any]=1	,		UpperCamelCase_    :     List[str]=None	,		UpperCamelCase_    :     Any=False	,		UpperCamelCase_    :     str=False	,		UpperCamelCase_    :     Union[str, Any]=False	,		):


         """simple docstring"""



         __A             =		self._read_tsv(lowercase_			)
         if skip_first_row:
             __A             =		lines[1:]
         __A             =		[]
         __A             =		[]
         __A             =		[]
         for i, line in enumerate(lowercase_			):
             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(lowercase_			)
                 ids.append(lowercase_			)

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




     def 						lowerCAmelCase_       (			self    :     List[str]	,		UpperCamelCase_    :     Optional[Any]	,		UpperCamelCase_    :     int=None	,		UpperCamelCase_    :     Dict=None	,		UpperCamelCase_    :     Tuple=False	,		UpperCamelCase_    :     int=False			):


         """simple docstring"""



         if labels is not None and len(lowercase_			) != len(lowercase_			):
             raise ValueError(
                 F"Text and labels have mismatched lengths {len(lowercase_			)} and {len(lowercase_			)}"			)
         if ids is not None and len(lowercase_			) != len(lowercase_			):
             raise ValueError(F"Text and ids have mismatched lengths {len(lowercase_			)} and {len(lowercase_			)}"			)
         if ids is None:
             __A             =		[None] * len(lowercase_			)
         if labels is None:
             __A             =		[None] * len(lowercase_			)
         __A             =		[]
         __A             =		set()
         for text_or_text_and_label, label, guid in zip(lowercase_	,		lowercase_	,		lowercase_			):
             if isinstance(lowercase_	,		(tuple, list)			) and label is None:
                 __A      ,		__A             =		text_or_text_and_label
             else:
                 __A             =		text_or_text_and_label
             added_labels.add(lowercase_			)
             examples.append(InputExample(guid=lowercase_	,		text_a=lowercase_	,		text_b=lowercase_	,		label=lowercase_			)			)

         # Update examples
         if overwrite_examples:
             __A             =		examples
         else:
             self.examples.extend(lowercase_			)

         # Update labels
         if overwrite_labels:
             __A             =		list(lowercase_			)
         else:
             __A             =		list(set(self.labels			).union(lowercase_			)			)

         return self.examples





     def 						lowerCAmelCase_       (			self    :     Union[str, Any]	,		UpperCamelCase_    :     List[str]	,		UpperCamelCase_    :     Tuple=None	,		UpperCamelCase_    :     List[Any]=False	,		UpperCamelCase_    :     Optional[int]=0	,		UpperCamelCase_    :     Dict=True	,		UpperCamelCase_    :     Any=None	,		):


         """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 % 10_000 == 0:
                 logger.info(F"Tokenizing example {ex_index}"			)

             __A             =		tokenizer.encode(
                 example.text_a	,		add_special_tokens=lowercase_	,		max_length=min(lowercase_	,		tokenizer.max_len			)	,		)
             all_input_ids.append(lowercase_			)

         __A             =		max(len(lowercase_			) for input_ids in all_input_ids			)

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

             # Zero-pad up to the sequence length.
             __A             =		batch_length - len(lowercase_			)
             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(lowercase_			) != batch_length:
                 raise ValueError(F"Error with input length {len(lowercase_			)} vs {batch_length}"			)
             if len(lowercase_			) != batch_length:
                 raise ValueError(F"Error with input length {len(lowercase_			)} 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(lowercase_			) for x in input_ids]			)}"			)
                 logger.info(F"attention_mask: {' '.join([str(lowercase_			) for x in attention_mask]			)}"			)
                 logger.info(F"label: {example.label} (id = {label})"			)

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

         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(
                 lowercase_	,		({"""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(lowercase_	,		lowercase_	,		lowercase_			)
             return dataset
         else:
             raise ValueError("""return_tensors should be one of \'tf\' or \'pt\'"""			)












from ..utils import DummyObject, requires_backends


__a :    int								=				None



def      _SCREAMING_SNAKE_CASE  (	*__lowercase				:     Any     ,			**__lowercase				:     Optional[int]      )  -> Tuple:





    """simple docstring"""

    requires_backends(__lowercase     ,			["""sentencepiece""", """tokenizers"""]      )




