





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


__UpperCAmelCase			:  Union[str, Any]										=				logging.get_logger(__name__)







@dataclass
class        __snake_case			:





       '''simple docstring'''







       lowerCAmelCase__ 						=	42
       lowerCAmelCase__ 						=	42
       lowerCAmelCase__ 						=	None
       lowerCAmelCase__ 						=	None




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







class        __snake_case			:





       '''simple docstring'''







       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       List[Any]						):
         raise NotImplementedError()




       def 				UpperCAmelCase__					(    self					:       Optional[int]				,							A					:       Union[str, Any]						):
         raise NotImplementedError()




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       str						):
         raise NotImplementedError()




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       List[str]						):
         raise NotImplementedError()




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         raise NotImplementedError()




       def 				UpperCAmelCase__					(    self					:       Optional[Any]				,							A					:       Optional[int]						):
         if len(self.get_labels()						) > 1:
           __snake_case:		Dict					    =		self.get_labels()[int(example.label						)]
         return example






       @classmethod
       def 				UpperCAmelCase__					(    cls					:       Dict				,							A					:       int				,							A					:       str=None						):
         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					:       Tuple				,							A					:       Dict=None				,							A					:       int=None				,							A					:       List[str]="classification"				,							A					:       Tuple=False						):
         __snake_case:		List[str]					    =		[] if labels is None else labels
         __snake_case:		int					    =		[] if examples is None else examples
         __snake_case:		Optional[int]					    =		mode
         __snake_case:		List[str]					    =		verbose




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




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




       @classmethod
       def 				UpperCAmelCase__					(    cls					:       Tuple				,							A					:       Optional[Any]				,							A					:       str=""				,							A					:       Optional[Any]=0				,							A					:       str=1				,							A					:       List[Any]=None				,							A					:       Optional[Any]=False				,							**A					:       Any						):
         __snake_case:		int					    =		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 				UpperCAmelCase__					(    cls					:       Optional[Any]				,							A					:       int				,							A					:       str=None				,							**A					:       str						):
         __snake_case:		List[Any]					    =		cls(**A						)
         processor.add_examples(A				,							labels=A						)
         return processor




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       Optional[Any]				,							A					:       str=""				,							A					:       Dict=0				,							A					:       Optional[Any]=1				,							A					:       Tuple=None				,							A					:       Any=False				,							A					:       str=False				,							A					:       Tuple=False				,							):
         __snake_case:		List[Any]					    =		self._read_tsv(A						)
         if skip_first_row:
           __snake_case:		Union[str, Any]					    =		lines[1:]
         __snake_case:		Optional[int]					    =		[]
         __snake_case:		Union[str, Any]					    =		[]
         __snake_case:		Tuple					    =		[]
         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:		Dict					    =		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 				UpperCAmelCase__					(    self					:       Any				,							A					:       List[str]				,							A					:       Optional[Any]=None				,							A					:       Tuple=None				,							A					:       List[str]=False				,							A					:       List[str]=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:		List[str]					    =		[None] * len(A						)
         if labels is None:
           __snake_case:		Optional[int]					    =		[None] * len(A						)
         __snake_case:		Optional[int]					    =		[]
         __snake_case:		List[str]					    =		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    ,  __snake_case:		Union[str, Any]					    =		text_or_text_and_label
           else:
             __snake_case:		Dict					    =		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:		List[Any]					    =		examples
         else:
           self.examples.extend(A						)

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

         return self.examples






       def 				UpperCAmelCase__					(    self					:       str				,							A					:       Optional[Any]				,							A					:       List[Any]=None				,							A					:       Optional[int]=False				,							A					:       Union[str, Any]=0				,							A					:       Optional[int]=True				,							A					:       int=None				,							):
         if max_length is None:
           __snake_case:		List[Any]					    =		tokenizer.max_len

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

         __snake_case:		str					    =		[]
         for ex_index, example in enumerate(self.examples						):
           if ex_index % 10_000 == 0:
             logger.info(f'''Tokenizing example {ex_index}'''						)

           __snake_case:		int					    =		tokenizer.encode(
               example.text_a				,							add_special_tokens=A				,							max_length=min(A				,							tokenizer.max_len						)				,							)
           all_input_ids.append(A						)

         __snake_case:		List[Any]					    =		max(len(A						) for input_ids in all_input_ids						)

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

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

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













from functools import lru_cache







@lru_cache
def      A__				(       SCREAMING_SNAKE_CASE__)  ->       int:
  if not isinstance(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__):
    raise TypeError("""k must be an integer.""")
  if k < 0:
    raise ValueError("""k integer must be greater or equal to zero.""")
  if k == 0:
    return 0
  elif k == 1:
    return 1
  else:
    return fibonacci(k - 1) + fibonacci(k - 2)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       int:
  __snake_case:		str					    =		len(SCREAMING_SNAKE_CASE__)
  # Find m such that F_m >= n where F_i is the i_th fibonacci number.
  __snake_case:		Optional[int]					    =		0
  while True:
    if fibonacci(SCREAMING_SNAKE_CASE__) >= len_list:
      __snake_case:		str					    =		i
      break
    i += 1
  __snake_case:		Tuple					    =		0
  while fibb_k > 0:
    __snake_case:		str					    =		min(
        offset + fibonacci(fibb_k - 1)   ,   len_list - 1)  # Prevent out of range
    __snake_case:		List[Any]					    =		arr[index_k]
    if item_k_1 == val:
      return index_k
    elif val < item_k_1:
      fibb_k -= 1
    elif val > item_k_1:
      offset += fibonacci(fibb_k - 1)
      fibb_k -= 2
  else:
    return -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()






