


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
    import torch

    from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
         UpperCAmelCase__										=     get_activation("gelu" )
         self.assertTrue(torch.allclose(gelu_python(__snake_case )		,						torch_builtin(__snake_case ) ) )
         self.assertFalse(torch.allclose(gelu_python(__snake_case )		,						gelu_new(__snake_case ) ) )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     torch.tensor([-1_00, -1, -0.1, 0, 0.1, 1.0, 1_00] )
         UpperCAmelCase__										=     get_activation("gelu" )
         UpperCAmelCase__										=     get_activation("gelu_10" )

         UpperCAmelCase__										=     torch_builtin(__snake_case )
         UpperCAmelCase__										=     geluaa(__snake_case )

         UpperCAmelCase__										=     torch.where(y_gelu_aa < 10.0		,						1		,						0 )

         self.assertTrue(torch.max(__snake_case ).item() == 10.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask		,						y_gelu_aa * clipped_mask ) )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         get_activation("gelu" )
         get_activation("gelu_10" )
         get_activation("gelu_fast" )
         get_activation("gelu_new" )
         get_activation("gelu_python" )
         get_activation("gelu_pytorch_tanh" )
         get_activation("linear" )
         get_activation("mish" )
         get_activation("quick_gelu" )
         get_activation("relu" )
         get_activation("sigmoid" )
         get_activation("silu" )
         get_activation("swish" )
         get_activation("tanh" )
         with self.assertRaises(__snake_case ):
                get_activation("bogus" )
         with self.assertRaises(__snake_case ):
                get_activation(__snake_case )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     get_activation("gelu" )
         UpperCAmelCase__										=     1
         UpperCAmelCase__										=     get_activation("gelu" )
         self.assertEqual(acta.a		,						1 )
         with self.assertRaises(__snake_case ):
                UpperCAmelCase__										=     acta.a









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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





