


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_				:			Optional[Any]   =	{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Union[str, Any]   =	['PLBartTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			List[str]   =	[
       'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PLBartForCausalLM',
       'PLBartForConditionalGeneration',
       'PLBartForSequenceClassification',
       'PLBartModel',
       'PLBartPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase_				:			Union[str, Any]   =	_LazyModule(__name__, globals()['__file__'], _import_structure)






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 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[str]:
             a_ :		List[str]							=  torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]					)
             a_ :		Union[str, Any]							=  get_activation('gelu'					)
             self.assertTrue(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE__					)					,							torch_builtin(SCREAMING_SNAKE_CASE__					)					)					)
             self.assertFalse(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE__					)					,							gelu_new(SCREAMING_SNAKE_CASE__					)					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Any:
             a_ :		List[str]							=  torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]					)
             a_ :		Union[str, Any]							=  get_activation('gelu'					)
             a_ :		str							=  get_activation('gelu_10'					)

             a_ :		Tuple							=  torch_builtin(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  geluaa(SCREAMING_SNAKE_CASE__					)

             a_ :		List[Any]							=  torch.where(y_gelu_aa < 10.0					,							1					,							0					)

             self.assertTrue(torch.max(SCREAMING_SNAKE_CASE__					).item() == 10.0					)
             self.assertTrue(torch.allclose(y_gelu * clipped_mask					,							y_gelu_aa * clipped_mask					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[Any]:
             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(SCREAMING_SNAKE_CASE__					):
                    get_activation('bogus'					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    get_activation(SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      str:
             a_ :		Any							=  get_activation('gelu'					)
             a_ :		Any							=  1
             a_ :		int							=  get_activation('gelu'					)
             self.assertEqual(acta.a					,							1					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    a_ :		Tuple							=  acta.a


