






'''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


__A   :							Tuple     	=						logging.get_logger(__name__)






@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	42
     _snake_case							   =	42
     _snake_case							   =	None
     _snake_case							   =	None






     def      _UpperCamelCase (self     :						List[Any]       )	->							Optional[Any]:
           return json.dumps(dataclasses.asdict(self       )					,					indent=2       ) + "\n"







@dataclass(frozen=lowerCamelCase    )
class        a__     :
     '''simple docstring'''







     _snake_case							   =	42
     _snake_case							   =	None
     _snake_case							   =	None
     _snake_case							   =	None






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







class        a__     :
     '''simple docstring'''







     def      _UpperCamelCase (self     :						Any					,					lowercase__     :						Dict       )	->							str:
           raise NotImplementedError()






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						Optional[Any]       )	->							str:
           raise NotImplementedError()






     def      _UpperCamelCase (self     :						Any					,					lowercase__     :						Dict       )	->							Optional[int]:
           raise NotImplementedError()






     def      _UpperCamelCase (self     :						Dict					,					lowercase__     :						Optional[Any]       )	->							List[str]:
           raise NotImplementedError()






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






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						Any       )	->							Dict:
           if len(self.get_labels()       ) > 1:
                 __A		 =   self.get_labels()[int(example.label       )]
           return example






     @classmethod
     def      _UpperCamelCase (cls     :						List[str]					,					lowercase__     :						str					,					lowercase__     :						Tuple=None       )	->							Optional[int]:
           with open(lowercase__					,					"r"					,					encoding="utf-8-sig"       ) as f:
                 return list(csv.reader(lowercase__					,					delimiter="\t"					,					quotechar=lowercase__       )       )







class        a__     (      lowerCamelCase    ):
     '''simple docstring'''







     def __init__(self     :						Tuple					,					lowercase__     :						str=None					,					lowercase__     :						Optional[int]=None					,					lowercase__     :						Dict="classification"					,					lowercase__     :						Optional[int]=False       )	->							List[Any]:
           __A		 =   [] if labels is None else labels
           __A		 =   [] if examples is None else examples
           __A		 =   mode
           __A		 =   verbose






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






     def __getitem__(self     :						Optional[Any]					,					lowercase__     :						str       )	->							Union[str, Any]:
           if isinstance(lowercase__					,					lowercase__       ):
                 return SingleSentenceClassificationProcessor(labels=self.labels					,					examples=self.examples[idx]       )
           return self.examples[idx]






     @classmethod
     def      _UpperCamelCase (cls     :						Tuple					,					lowercase__     :						Tuple					,					lowercase__     :						List[str]=""					,					lowercase__     :						int=0					,					lowercase__     :						Union[str, Any]=1					,					lowercase__     :						List[Any]=None					,					lowercase__     :						Any=False					,					**lowercase__     :						List[Any]       )	->							Tuple:
           __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      _UpperCamelCase (cls     :						Tuple					,					lowercase__     :						str					,					lowercase__     :						str=None					,					**lowercase__     :						str       )	->							str:
           __A		 =   cls(**lowercase__       )
           processor.add_examples(lowercase__					,					labels=lowercase__       )
           return processor






     def      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						Tuple					,					lowercase__     :						List[Any]=""					,					lowercase__     :						Dict=0					,					lowercase__     :						Tuple=1					,					lowercase__     :						str=None					,					lowercase__     :						int=False					,					lowercase__     :						int=False					,					lowercase__     :						Tuple=False					,					)	->							List[Any]:
           __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      _UpperCamelCase (self     :						List[str]					,					lowercase__     :						Optional[int]					,					lowercase__     :						Union[str, Any]=None					,					lowercase__     :						List[str]=None					,					lowercase__     :						Tuple=False					,					lowercase__     :						List[str]=False       )	->							Optional[Any]:
           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      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						int					,					lowercase__     :						List[str]=None					,					lowercase__     :						Optional[int]=False					,					lowercase__     :						Tuple=0					,					lowercase__     :						Optional[int]=True					,					lowercase__     :						Any=None					,					)	->							Optional[int]:
           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 % 10000 == 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 % 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		 =   [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'"       )










'''simple docstring'''







def     _UpperCamelCase				(  snake_case__ = 1_000				):
      __A		 =   []
      __A		 =   []
      __A		 =   3
      __A		 =   5
      __A		 =   1
      while True:
            __A		 =   z * temp
            if result < n:
                  zmulti.append(snake_case__				)
                  temp += 1
            else:
                  __A		 =   1
                  break
      while True:
            __A		 =   x * temp
            if result < n:
                  xmulti.append(snake_case__				)
                  temp += 1
            else:
                  break
      __A		 =   list(set(xmulti + zmulti				)				)
      return sum(snake_case__				)


if __name__ == "__main__":
       print(F"""{solution() = }""")


