






"""simple docstring"""
from typing import TYPE_CHECKING

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


snake_case   :   List[Any]    		=		{
    """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   :   int    		=		["""DebertaV2TokenizerFast"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   List[str]    		=		[
     """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   :   Tuple    		=		[
     """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   :   Tuple    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







"""simple docstring"""
import numpy as np


def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:

       """simple docstring"""





       __magic_name__										=						n_draws * class_counts / class_counts.sum()
       # floored means we don't overshoot n_samples, but probably undershoot
       __magic_name__										=						np.floor(__snake_case     )
       # we add samples according to how much "left over" probability
       # they had, until we arrive at n_samples
       __magic_name__										=						int(n_draws - floored.sum()     )
       if need_to_add > 0:
              __magic_name__										=						continuous - floored
              __magic_name__										=						np.sort(np.unique(__snake_case     )     )[::-1]
              # add according to remainder, but break ties
              # randomly to avoid biases
              for value in values:
                     ((__magic_name__)							,     )										=						np.where(remainder == value     )
                     # if we need_to_add less than what's in inds
                     # we draw randomly from them.
                     # if we need to add more, we add them all and
                     # go to the next value
                     __magic_name__										=						min(len(__snake_case     )   ,    __snake_case     )
                     __magic_name__										=						rng.choice(__snake_case   ,    size=__snake_case   ,    replace=__snake_case     )
                     floored[inds] += 1
                     need_to_add -= add_now
                     if need_to_add == 0:
                            break
       return floored.astype(np.intaa     )






def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:

       """simple docstring"""





       __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
       __magic_name__										=						classes.shape[0]
       __magic_name__										=						np.bincount(__snake_case     )
       if np.min(__snake_case     ) < 2:
              raise ValueError('Minimum class count error'     )
       if n_train < n_classes:
              raise ValueError(
                  'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)     )
       if n_test < n_classes:
              raise ValueError(
                  'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)     )
       __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
       for _ in range(__snake_case     ):
              __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
              __magic_name__										=						class_counts - n_i
              __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )

              __magic_name__										=						[]
              __magic_name__										=						[]

              for i in range(__snake_case     ):
                     __magic_name__										=						rng.permutation(class_counts[i]     )
                     __magic_name__										=						class_indices[i].take(__snake_case   ,    mode='clip'     )
                     train.extend(perm_indices_class_i[: n_i[i]]     )
                     test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]     )
              __magic_name__										=						rng.permutation(__snake_case     )
              __magic_name__										=						rng.permutation(__snake_case     )

              yield train, test