





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





@dataclass
class 						_A:


    """simple docstring"""


    UpperCamelCase   :  str
    UpperCamelCase   :  str
    UpperCamelCase   :  Optional[str]    =     None
    UpperCamelCase   :  Optional[str]    =     None



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





@dataclass(frozen=SCREAMING_SNAKE_CASE__       )
class 						_A:


    """simple docstring"""


    UpperCamelCase   :  List[int]
    UpperCamelCase   :  Optional[List[int]]    =     None
    UpperCamelCase   :  Optional[List[int]]    =     None
    UpperCamelCase   :  Optional[Union[int, float]]    =     None



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





class 						_A:


    """simple docstring"""


    def  UpperCAmelCase_		(      self					, _A					):
         raise NotImplementedError()



    def  UpperCAmelCase_		(      self					, _A					):
         raise NotImplementedError()



    def  UpperCAmelCase_		(      self					, _A					):
         raise NotImplementedError()



    def  UpperCAmelCase_		(      self					, _A					):
         raise NotImplementedError()



    def  UpperCAmelCase_		(      self					):
         raise NotImplementedError()



    def  UpperCAmelCase_		(      self					, _A					):

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





    @classmethod
    def  UpperCAmelCase_		(      cls					, _A					, _A=None					):

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





class 						_A( SCREAMING_SNAKE_CASE__       ):


    """simple docstring"""


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



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



    def __getitem__(      self					, _A					):

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



    @classmethod
    def  UpperCAmelCase_		(      cls					, _A					, _A=""					, _A=0					, _A=1					, _A=None					, _A=False					, **_A					):
         __A     :						List[str]          =    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					, _A					, _A=None					, **_A					):
         __A     :						List[Any]          =    cls(**_A					)
         processor.add_examples(_A					, labels=_A					)
         return processor



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

         # Update labels
         if overwrite_labels:
              __A     :						Tuple          =    list(_A					)
         else:
              __A     :						Optional[int]          =    list(set(self.labels					).union(_A					)					)

         return self.examples





    def  UpperCAmelCase_		(      self					, _A					, _A=None					, _A=False					, _A=0					, _A=True					, _A=None					, ):

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

         __A     :						Optional[int]          =    {label: i for i, label in enumerate(self.labels					)}

         __A     :						List[str]          =    []
         for ex_index, example in enumerate(self.examples					):
              if ex_index % 10000 == 0:
                   logger.info(F"""Tokenizing example {ex_index}"""					)

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

         __A     :						List[str]          =    max(len(_A					) for input_ids in all_input_ids					)

         __A     :						Optional[Any]          =    []
         for ex_index, (input_ids, example) in enumerate(zip(_A					, 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     :						int          =    [1 if mask_padding_with_zero else 0] * len(_A					)

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

              __A     :						List[Any]          =    TensorDataset(_A					, _A					, _A					)
              return dataset
         else:
              raise ValueError('return_tensors should be one of \'tf\' or \'pt\''					)











from __future__ import annotations

import math
import random







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				list[int]:
					__A     :						int          =    d * e - 1
					__A     :						List[str]          =    0
					__A     :						Optional[Any]          =    0
					while p == 0:
										__A     :						List[Any]          =    random.randint(2 ,						n - 1  )
										__A     :						Optional[int]          =    k
										while True:
															if t % 2 == 0:
																				__A     :						Tuple          =    t // 2
																				__A     :						List[str]          =    (g**t) % n
																				__A     :						List[Any]          =    math.gcd(x - 1 ,						a  )
																				if x > 1 and y > 1:
																									__A     :						List[str]          =    y
																									__A     :						str          =    n // y
																									break  # find the correct factors
															else:
																				break  # t is not divisible by 2, break and choose another g
					return sorted([p, q]  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




