






"""simple docstring"""



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        a     (   unittest.TestCase	):







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]       )
      lowercase							    =       get_activation('gelu'       )
      self.assertTrue(torch.allclose(gelu_python(_lowerCamelCase       )							,		torch_builtin(_lowerCamelCase       )       )       )
      self.assertFalse(torch.allclose(gelu_python(_lowerCamelCase       )							,		gelu_new(_lowerCamelCase       )       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]       )
      lowercase							    =       get_activation('gelu'       )
      lowercase							    =       get_activation('gelu_10'       )

      lowercase							    =       torch_builtin(_lowerCamelCase       )
      lowercase							    =       geluaa(_lowerCamelCase       )

      lowercase							    =       torch.where(y_gelu_aa < 1_0.0							,		1							,		0       )

      self.assertTrue(torch.max(_lowerCamelCase       ).item() == 1_0.0       )
      self.assertTrue(torch.allclose(y_gelu * clipped_mask							,		y_gelu_aa * clipped_mask       )       )







    def 			UpperCamelCase_      (			self       ):
      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(_lowerCamelCase       ):
        get_activation('bogus'       )
      with self.assertRaises(_lowerCamelCase       ):
        get_activation(_lowerCamelCase       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       get_activation('gelu'       )
      lowercase							    =       1
      lowercase							    =       get_activation('gelu'       )
      self.assertEqual(acta.a							,		1       )
      with self.assertRaises(_lowerCamelCase       ):
        lowercase							    =       acta.a










"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase		:  Dict						=		{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  str						=		['RobertaTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  Tuple						=		[
        'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RobertaForCausalLM',
        'RobertaForMaskedLM',
        'RobertaForMultipleChoice',
        'RobertaForQuestionAnswering',
        'RobertaForSequenceClassification',
        'RobertaForTokenClassification',
        'RobertaModel',
        'RobertaPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  List[Any]						=		[
        'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRobertaForCausalLM',
        'TFRobertaForMaskedLM',
        'TFRobertaForMultipleChoice',
        'TFRobertaForQuestionAnswering',
        'TFRobertaForSequenceClassification',
        'TFRobertaForTokenClassification',
        'TFRobertaMainLayer',
        'TFRobertaModel',
        'TFRobertaPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  Optional[int]						=		[
        'FlaxRobertaForCausalLM',
        'FlaxRobertaForMaskedLM',
        'FlaxRobertaForMultipleChoice',
        'FlaxRobertaForQuestionAnswering',
        'FlaxRobertaForSequenceClassification',
        'FlaxRobertaForTokenClassification',
        'FlaxRobertaModel',
        'FlaxRobertaPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
    from .tokenization_roberta import RobertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_roberta_fast import RobertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta import (
            ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaForCausalLM,
            RobertaForMaskedLM,
            RobertaForMultipleChoice,
            RobertaForQuestionAnswering,
            RobertaForSequenceClassification,
            RobertaForTokenClassification,
            RobertaModel,
            RobertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta import (
            TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaForCausalLM,
            TFRobertaForMaskedLM,
            TFRobertaForMultipleChoice,
            TFRobertaForQuestionAnswering,
            TFRobertaForSequenceClassification,
            TFRobertaForTokenClassification,
            TFRobertaMainLayer,
            TFRobertaModel,
            TFRobertaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta import (
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaModel,
            FlaxRobertaPreTrainedModel,
        )

else:
    import sys

    _UpperCamelCase		:  Optional[Any]						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


