import numpy as np





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      =n_draws * class_counts / class_counts.sum()
      # floored means we don't overshoot n_samples, but probably undershoot
      UpperCAmelCase    : List[Any]      =np.floor(__lowerCAmelCase						)
      # we add samples according to how much "left over" probability
      # they had, until we arrive at n_samples
      UpperCAmelCase    : Any      =int(n_draws - floored.sum()						)
      if need_to_add > 0:
            UpperCAmelCase    : Optional[int]      =continuous - floored
            UpperCAmelCase    : str      =np.sort(np.unique(__lowerCAmelCase						)						)[::-1]
            # add according to remainder, but break ties
            # randomly to avoid biases
            for value in values:
                  (UpperCAmelCase							)    : Any      =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
                  UpperCAmelCase    : List[Any]      =min(len(__lowerCAmelCase						)		,					__lowerCAmelCase						)
                  UpperCAmelCase    : List[str]      =rng.choice(__lowerCAmelCase		,					size=__lowerCAmelCase		,					replace=__lowerCAmelCase						)
                  floored[inds] += 1
                  need_to_add -= add_now
                  if need_to_add == 0:
                        break
      return floored.astype(np.intaa						)





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase=10						)->  str:






      '''simple docstring'''





      UpperCAmelCase    : Dict      =np.unique(__lowerCAmelCase		,					return_inverse=__lowerCAmelCase						)
      UpperCAmelCase    : Union[str, Any]      =classes.shape[0]
      UpperCAmelCase    : List[Any]      =np.bincount(__lowerCAmelCase						)
      if np.min(__lowerCAmelCase						) < 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)						)
      UpperCAmelCase    : List[str]      =np.split(np.argsort(__lowerCAmelCase		,					kind='''mergesort'''						)		,					np.cumsum(__lowerCAmelCase						)[:-1]						)
      for _ in range(__lowerCAmelCase						):
            UpperCAmelCase    : List[Any]      =approximate_mode(__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)
            UpperCAmelCase    : str      =class_counts - n_i
            UpperCAmelCase    : Tuple      =approximate_mode(__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)

            UpperCAmelCase    : Any      =[]
            UpperCAmelCase    : Tuple      =[]

            for i in range(__lowerCAmelCase						):
                  UpperCAmelCase    : Tuple      =rng.permutation(class_counts[i]						)
                  UpperCAmelCase    : Optional[int]      =class_indices[i].take(__lowerCAmelCase		,					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]]						)
            UpperCAmelCase    : Optional[int]      =rng.permutation(__lowerCAmelCase						)
            UpperCAmelCase    : int      =rng.permutation(__lowerCAmelCase						)

            yield train, test

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case			      =    {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''EsmForMaskedLM''',
         '''EsmForSequenceClassification''',
         '''EsmForTokenClassification''',
         '''EsmModel''',
         '''EsmPreTrainedModel''',
     ]
     __snake_case			      =    ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case			      =    [
         '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFEsmForMaskedLM''',
         '''TFEsmForSequenceClassification''',
         '''TFEsmForTokenClassification''',
         '''TFEsmModel''',
         '''TFEsmPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
     from .tokenization_esm import EsmTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_esm import (
              ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              EsmForMaskedLM,
              EsmForSequenceClassification,
              EsmForTokenClassification,
              EsmModel,
              EsmPreTrainedModel,
          )
          from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_esm import (
              TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFEsmForMaskedLM,
              TFEsmForSequenceClassification,
              TFEsmForTokenClassification,
              TFEsmModel,
              TFEsmPreTrainedModel,
          )


else:
     import sys

     __snake_case			      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
