

"""simple docstring"""


import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)

class      __a							(unittest.TestCase):






     '''simple docstring'''

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""a""", """b""", """c"""]

           # Defaults to last layer if both are None
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	get_aligned_output_features_output_indices(_a	,    _a	,    _a  )
           self.assertEqual(_a	,    ["""c"""]  )
           self.assertEqual(_a	,    [2]  )

           # Out indices set to match out features
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	get_aligned_output_features_output_indices(["""a""", """c"""]	,    _a	,    _a  )
           self.assertEqual(_a	,    ["""a""", """c"""]  )
           self.assertEqual(_a	,    [0, 2]  )

           # Out features set to match out indices
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	get_aligned_output_features_output_indices(_a	,    [0, 2]	,    _a  )
           self.assertEqual(_a	,    ["""a""", """c"""]  )
           self.assertEqual(_a	,    [0, 2]  )

           # Out features selected from negative indices
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	get_aligned_output_features_output_indices(_a	,    [-3, -1]	,    _a  )
           self.assertEqual(_a	,    ["""a""", """c"""]  )
           self.assertEqual(_a	,    [-3, -1]  )

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           with self.assertRaises(_a  ):
                 verify_out_features_out_indices(["""a""", """b"""]	,    (0, 1)	,    _a  )

           # Out features must be a list
           with self.assertRaises(_a  ):
                 verify_out_features_out_indices(("""a""", """b""")	,    (0, 1)	,    ["""a""", """b"""]  )

           # Out features must be a subset of stage names
           with self.assertRaises(_a  ):
                 verify_out_features_out_indices(["""a""", """b"""]	,    (0, 1)	,    ["""a"""]  )

           # Out indices must be a list or tuple
           with self.assertRaises(_a  ):
                 verify_out_features_out_indices(_a	,    0	,    ["""a""", """b"""]  )

           # Out indices must be a subset of stage names
           with self.assertRaises(_a  ):
                 verify_out_features_out_indices(_a	,    (0, 1)	,    ["""a"""]  )

           # Out features and out indices must be the same length
           with self.assertRaises(_a  ):
                 verify_out_features_out_indices(["""a""", """b"""]	,    (0,)	,    ["""a""", """b""", """c"""]  )

           # Out features should match out indices
           with self.assertRaises(_a  ):
                 verify_out_features_out_indices(["""a""", """b"""]	,    (0, 2)	,    ["""a""", """b""", """c"""]  )

           # Out features and out indices should be in order
           with self.assertRaises(_a  ):
                 verify_out_features_out_indices(["""b""", """a"""]	,    (0, 1)	,    ["""a""", """b"""]  )

           # Check passes with valid inputs
           verify_out_features_out_indices(["""a""", """b""", """d"""]	,    (0, 1, -1)	,    ["""a""", """b""", """c""", """d"""]  )





     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	BackboneMixin()

           SCREAMING_SNAKE_CASE__   :      str      		=	["""a""", """b""", """c"""]
           SCREAMING_SNAKE_CASE__   :      str      		=	["""a""", """c"""]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[0, 2]

           # Check that the output features and indices are set correctly
           self.assertEqual(backbone.out_features	,    ["""a""", """c"""]  )
           self.assertEqual(backbone.out_indices	,    [0, 2]  )

           # Check out features and indices are updated correctly
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	["""a""", """b"""]
           self.assertEqual(backbone.out_features	,    ["""a""", """b"""]  )
           self.assertEqual(backbone.out_indices	,    [0, 1]  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[-3, -1]
           self.assertEqual(backbone.out_features	,    ["""a""", """c"""]  )
           self.assertEqual(backbone.out_indices	,    [-3, -1]  )





"""simple docstring"""


from typing import TYPE_CHECKING

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


a					:Any    = {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Union[str, Any]    = [
      "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RobertaPreLayerNormForCausalLM",
      "RobertaPreLayerNormForMaskedLM",
      "RobertaPreLayerNormForMultipleChoice",
      "RobertaPreLayerNormForQuestionAnswering",
      "RobertaPreLayerNormForSequenceClassification",
      "RobertaPreLayerNormForTokenClassification",
      "RobertaPreLayerNormModel",
      "RobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:Optional[Any]    = [
      "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRobertaPreLayerNormForCausalLM",
      "TFRobertaPreLayerNormForMaskedLM",
      "TFRobertaPreLayerNormForMultipleChoice",
      "TFRobertaPreLayerNormForQuestionAnswering",
      "TFRobertaPreLayerNormForSequenceClassification",
      "TFRobertaPreLayerNormForTokenClassification",
      "TFRobertaPreLayerNormMainLayer",
      "TFRobertaPreLayerNormModel",
      "TFRobertaPreLayerNormPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a					:List[Any]    = [
      "FlaxRobertaPreLayerNormForCausalLM",
      "FlaxRobertaPreLayerNormForMaskedLM",
      "FlaxRobertaPreLayerNormForMultipleChoice",
      "FlaxRobertaPreLayerNormForQuestionAnswering",
      "FlaxRobertaPreLayerNormForSequenceClassification",
      "FlaxRobertaPreLayerNormForTokenClassification",
      "FlaxRobertaPreLayerNormModel",
      "FlaxRobertaPreLayerNormPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
      RobertaPreLayerNormConfig,
      RobertaPreLayerNormOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        RobertaPreLayerNormForCausalLM,
        RobertaPreLayerNormForMaskedLM,
        RobertaPreLayerNormForMultipleChoice,
        RobertaPreLayerNormForQuestionAnswering,
        RobertaPreLayerNormForSequenceClassification,
        RobertaPreLayerNormForTokenClassification,
        RobertaPreLayerNormModel,
        RobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roberta_prelayernorm import (
        TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRobertaPreLayerNormForCausalLM,
        TFRobertaPreLayerNormForMaskedLM,
        TFRobertaPreLayerNormForMultipleChoice,
        TFRobertaPreLayerNormForQuestionAnswering,
        TFRobertaPreLayerNormForSequenceClassification,
        TFRobertaPreLayerNormForTokenClassification,
        TFRobertaPreLayerNormMainLayer,
        TFRobertaPreLayerNormModel,
        TFRobertaPreLayerNormPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
        FlaxRobertaPreLayerNormPreTrainedModel,
    )

else:
  import sys

  a					:Optional[int]    = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




