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


lowercase_   				=       logging.get_logger(__name__)







@dataclass
class 			SCREAMING_SNAKE_CASE__ :
       A				:     Optional[int]					    =     42
       A				:     Union[str, Any]					    =     42
       A				:     Tuple					    =     None
       A				:     Tuple					    =     None

       def        snake_case__  (				self :			Union[str, Any]       ):
           return json.dumps(dataclasses.asdict(self       )       ,       indent=2       ) + "\n"







@dataclass(frozen=__UpperCamelCase  )
class 			SCREAMING_SNAKE_CASE__ :
       A				:     List[str]					    =     42
       A				:     Optional[Any]					    =     None
       A				:     Optional[int]					    =     None
       A				:     str					    =     None

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







class 			SCREAMING_SNAKE_CASE__ :

       def        snake_case__  (				self :			Optional[int]       ,       _lowerCAmelCase :			Optional[Any]       ):
           raise NotImplementedError()

       def        snake_case__  (				self :			List[Any]       ,       _lowerCAmelCase :			Any       ):
           raise NotImplementedError()

       def        snake_case__  (				self :			Tuple       ,       _lowerCAmelCase :			Optional[Any]       ):
           raise NotImplementedError()

       def        snake_case__  (				self :			int       ,       _lowerCAmelCase :			List[str]       ):
           raise NotImplementedError()

       def        snake_case__  (				self :			Union[str, Any]       ):
           raise NotImplementedError()

       def        snake_case__  (				self :			Optional[Any]       ,       _lowerCAmelCase :			Any       ):


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





       @classmethod
       def        snake_case__  (				cls :			Dict       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			Optional[int]=None       ):


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







class 			SCREAMING_SNAKE_CASE__ (   __UpperCamelCase  ):

       def __init__(				self :			int       ,       _lowerCAmelCase :			Optional[Any]=None       ,       _lowerCAmelCase :			Dict=None       ,       _lowerCAmelCase :			Optional[Any]="classification"       ,       _lowerCAmelCase :			List[Any]=False       ):
           __snake_case    :							Dict             =  [] if labels is None else labels
           __snake_case    :							List[Any]             =  [] if examples is None else examples
           __snake_case    :							Optional[int]             =  mode
           __snake_case    :							Optional[int]             =  verbose

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

       def __getitem__(				self :			Any       ,       _lowerCAmelCase :			List[Any]       ):


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

       @classmethod
       def        snake_case__  (				cls :			Any       ,       _lowerCAmelCase :			int       ,       _lowerCAmelCase :			List[str]=""       ,       _lowerCAmelCase :			Any=0       ,       _lowerCAmelCase :			int=1       ,       _lowerCAmelCase :			Optional[Any]=None       ,       _lowerCAmelCase :			Union[str, Any]=False       ,       **_lowerCAmelCase :			Dict       ):
           __snake_case    :							Dict             =  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        snake_case__  (				cls :			Optional[Any]       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			List[str]=None       ,       **_lowerCAmelCase :			Optional[int]       ):
           __snake_case    :							Optional[int]             =  cls(**A_       )
           processor.add_examples(A_       ,       labels=A_       )
           return processor

       def        snake_case__  (				self :			Dict       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			Union[str, Any]=""       ,       _lowerCAmelCase :			Tuple=0       ,       _lowerCAmelCase :			List[str]=1       ,       _lowerCAmelCase :			int=None       ,       _lowerCAmelCase :			Optional[int]=False       ,       _lowerCAmelCase :			int=False       ,       _lowerCAmelCase :			int=False       ,       ):
           __snake_case    :							Any             =  self._read_tsv(A_       )
           if skip_first_row:
               __snake_case    :							Tuple             =  lines[1:]
           __snake_case    :							Any             =  []
           __snake_case    :							Union[str, Any]             =  []
           __snake_case    :							Union[str, Any]             =  []
           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:
                   __snake_case    :							Tuple             =  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        snake_case__  (				self :			Dict       ,       _lowerCAmelCase :			List[Any]       ,       _lowerCAmelCase :			List[str]=None       ,       _lowerCAmelCase :			Union[str, Any]=None       ,       _lowerCAmelCase :			int=False       ,       _lowerCAmelCase :			Union[str, Any]=False       ):


           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:
               __snake_case    :							Any             =  [None] * len(A_       )
           if labels is None:
               __snake_case    :							str             =  [None] * len(A_       )
           __snake_case    :							Any             =  []
           __snake_case    :							Union[str, Any]             =  set()
           for text_or_text_and_label, label, guid in zip(A_       ,       A_       ,       A_       ):
               if isinstance(A_       ,       (tuple, list)       ) and label is None:
                   __snake_case    :							str             =  text_or_text_and_label
               else:
                   __snake_case    :							Union[str, 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:
               __snake_case    :							int             =  examples
           else:
               self.examples.extend(A_       )

           # Update labels
           if overwrite_labels:
               __snake_case    :							List[Any]             =  list(A_       )
           else:
               __snake_case    :							int             =  list(set(self.labels       ).union(A_       )       )

           return self.examples





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


           if max_length is None:
               __snake_case    :							Optional[int]             =  tokenizer.max_len

           __snake_case    :							Union[str, Any]             =  {label: i for i, label in enumerate(self.labels       )}

           __snake_case    :							Optional[int]             =  []
           for ex_index, example in enumerate(self.examples       ):
               if ex_index % 1_00_00 == 0:
                   logger.info(f'''Tokenizing example {ex_index}'''       )

               __snake_case    :							Optional[Any]             =  tokenizer.encode(
                   example.text_a       ,       add_special_tokens=A_       ,       max_length=min(A_       ,       tokenizer.max_len       )       ,       )
               all_input_ids.append(A_       )

           __snake_case    :							List[str]             =  max(len(A_       ) for input_ids in all_input_ids       )

           __snake_case    :							Dict             =  []
           for ex_index, (input_ids, example) in enumerate(zip(A_       ,       self.examples       )       ):
               if ex_index % 1_00_00 == 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.
               __snake_case    :							List[str]             =  [1 if mask_padding_with_zero else 0] * len(A_       )

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

               __snake_case    :							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

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

               __snake_case    :							Optional[int]             =  TensorDataset(A_       ,       A_       ,       A_       )
               return dataset
           else:
               raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""       )






from __future__ import annotations

SCREAMING_SNAKE_CASE_						=					'Alexander Joslin'

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:

				if number <= 0:
								_UpperCAmelCase				:						Optional[Any]       						=			F'n must be greater than 0. Got n = {number}'
								raise ValueError(lowerCAmelCase       )

				_UpperCAmelCase				:						Dict       						=			[number + 1] * (number + 1)

				# starting position
				_UpperCAmelCase				:						Dict       						=			0
				for i in range(1     ,					lowerCAmelCase       ):
								_UpperCAmelCase				:						int       						=			min(table[i + 1]     ,					table[i] + 1       )
								# check if out of bounds
								if i * 2 <= number:
												_UpperCAmelCase				:						int       						=			min(table[i * 2]     ,					table[i] + 1       )
								# check if out of bounds
								if i * 3 <= number:
												_UpperCAmelCase				:						int       						=			min(table[i * 3]     ,					table[i] + 1       )
				return table[number]


if __name__ == "__main__":
					import doctest

					doctest.testmod()


