


from typing import TYPE_CHECKING

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


snake_case       :					str        =  {
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case       :					Union[str, Any]        =  ['''DebertaV2TokenizerFast''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case       :					Union[str, Any]        =  [
         '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFDebertaV2ForMaskedLM''',
         '''TFDebertaV2ForQuestionAnswering''',
         '''TFDebertaV2ForSequenceClassification''',
         '''TFDebertaV2ForTokenClassification''',
         '''TFDebertaV2Model''',
         '''TFDebertaV2PreTrainedModel''',
     ]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case       :					List[str]        =  [
         '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DebertaV2ForMaskedLM''',
         '''DebertaV2ForMultipleChoice''',
         '''DebertaV2ForQuestionAnswering''',
         '''DebertaV2ForSequenceClassification''',
         '''DebertaV2ForTokenClassification''',
         '''DebertaV2Model''',
         '''DebertaV2PreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_deberta_va import (
         DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DebertaVaConfig,
         DebertaVaOnnxConfig,
     )
     from .tokenization_deberta_va import DebertaVaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_deberta_va import (
              TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDebertaVaForMaskedLM,
              TFDebertaVaForQuestionAnswering,
              TFDebertaVaForSequenceClassification,
              TFDebertaVaForTokenClassification,
              TFDebertaVaModel,
              TFDebertaVaPreTrainedModel,
          )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deberta_va import (
              DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
              DebertaVaForMaskedLM,
              DebertaVaForMultipleChoice,
              DebertaVaForQuestionAnswering,
              DebertaVaForSequenceClassification,
              DebertaVaForTokenClassification,
              DebertaVaModel,
              DebertaVaPreTrainedModel,
          )

else:
     import sys

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









import numpy as np
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]		):




      """simple docstring"""





      a							,       a		:Union[str, Any]											= a.shape
      a		:Any											= min(UpperCAmelCase_				,						UpperCAmelCase_		)
      a		:Tuple											= np.eye(UpperCAmelCase_		)
      a		:Any											= a.copy()

      for k in range(t - 1		):
            # select a column of modified matrix A':
            a		:List[str]											= r[k:, [k]]
            # construct first basis vector
            a		:List[str]											= np.zeros_like(UpperCAmelCase_		)
            a		:List[Any]											= 1.0
            # determine scaling factor
            a		:Union[str, Any]											= np.linalg.norm(UpperCAmelCase_		)
            # construct vector v for Householder reflection
            a		:List[str]											= x + np.sign(x[0]		) * alpha * ea
            v /= np.linalg.norm(UpperCAmelCase_		)

            # construct the Householder matrix
            a		:Union[str, Any]											= np.eye(m - k		) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            a		:Tuple											= np.block([[np.eye(UpperCAmelCase_		), np.zeros((k, m - k)		)], [np.zeros((m - k, k)		), q_k]]		)

            a		:List[Any]											= q @ q_k.T
            a		:int											= q_k @ r

      return q, r


if __name__ == "__main__":
     import doctest

     doctest.testmod()





