

import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase__ :     Any				    =    logging.getLogger(__name__)







@dataclass
class        a__       (   __UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Optional[float]			       =field(
      default=0.0	,			metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}       )
  UpperCAmelCase__      : bool			       =field(default=__UpperCAmelCase	,			metadata={"""help""": """Whether to SortishSamler or not."""}       )
  UpperCAmelCase__      : bool			       =field(
      default=__UpperCAmelCase	,			metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}       )
  UpperCAmelCase__      : bool			       =field(default=__UpperCAmelCase	,			metadata={"""help""": """whether to use adafactor"""}       )
  UpperCAmelCase__      : Optional[float]			       =field(
      default=__UpperCAmelCase	,			metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}       )
  UpperCAmelCase__      : Optional[float]			       =field(
      default=__UpperCAmelCase	,			metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}       )
  UpperCAmelCase__      : Optional[float]			       =field(default=__UpperCAmelCase	,			metadata={"""help""": """Dropout probability. Goes into model.config."""}       )
  UpperCAmelCase__      : Optional[float]			       =field(
      default=__UpperCAmelCase	,			metadata={"""help""": """Attention dropout probability. Goes into model.config."""}       )
  UpperCAmelCase__      : Optional[str]			       =field(
      default="""linear"""	,			metadata={"""help""": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()       )}'''}	,			)







from __future__ import annotations

import numpy as np






def 					__lowercase      (     _A			)     ->						tuple[np.ndarray, np.ndarray]:
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   np.shape(_A			)
      if rows != columns:
            SCREAMING_SNAKE_CASE :      int       =   (
                """'table' has to be of square shaped array but got a """
                F"{rows}x{columns} array:\n{table}"
            )
            raise ValueError(_A			)

      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.zeros((rows, columns)			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.zeros((rows, columns)			)
      for i in range(_A			):
            for j in range(_A			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""			)
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (table[i][j] - total) / upper[j][j]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   1
            for j in range(_A						,    _A			):
                  SCREAMING_SNAKE_CASE :      List[str]       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   table[i][j] - total
      return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()




