






import numpy as np


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    SCREAMING_SNAKE_CASE__       							= n_draws * class_counts / class_counts.sum()
    # floored means we don't overshoot n_samples, but probably undershoot
    SCREAMING_SNAKE_CASE__       							= np.floor(lowerCAmelCase_  )
    # we add samples according to how much "left over" probability
    # they had, until we arrive at n_samples
    SCREAMING_SNAKE_CASE__       							= int(n_draws - floored.sum()  )
    if need_to_add > 0:
        SCREAMING_SNAKE_CASE__       							= continuous - floored
        SCREAMING_SNAKE_CASE__       							= np.sort(np.unique(lowerCAmelCase_  )  )[::-1]
        # add according to remainder, but break ties
        # randomly to avoid biases
        for value in values:
            ((SCREAMING_SNAKE_CASE__		)							,				)       							= 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
            SCREAMING_SNAKE_CASE__       							= min(len(lowerCAmelCase_  )   ,       lowerCAmelCase_  )
            SCREAMING_SNAKE_CASE__       							= 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        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_=1_0  )							->	List[Any]:
    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= np.unique(lowerCAmelCase_   ,       return_inverse=lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= classes.shape[0]
    SCREAMING_SNAKE_CASE__       							= 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)  )
    SCREAMING_SNAKE_CASE__       							= np.split(np.argsort(lowerCAmelCase_   ,       kind='''mergesort'''  )   ,       np.cumsum(lowerCAmelCase_  )[:-1]  )
    for _ in range(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= approximate_mode(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= class_counts - n_i
        SCREAMING_SNAKE_CASE__       							= approximate_mode(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )

        SCREAMING_SNAKE_CASE__       							= []
        SCREAMING_SNAKE_CASE__       							= []

        for i in range(lowerCAmelCase_  ):
            SCREAMING_SNAKE_CASE__       							= rng.permutation(class_counts[i]  )
            SCREAMING_SNAKE_CASE__       							= 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]]  )
        SCREAMING_SNAKE_CASE__       							= rng.permutation(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= rng.permutation(lowerCAmelCase_  )

        yield train, test







import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
  import transformer_engine.pytorch as te


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=True   ,       lowerCAmelCase_=True  )							->	Tuple:
    if not is_fpa_available():
        raise ImportError('''Using `convert_model` requires transformer_engine to be installed.'''  )
    for name, module in model.named_children():
        if isinstance(lowerCAmelCase_   ,       nn.Linear  ) and to_transformer_engine and _convert_linear:
            # Return early if the linear layer weights are not multiples of 16
            if any(p % 1_6 != 0 for p in module.weight.shape  ):
                return
            SCREAMING_SNAKE_CASE__       							= module.bias is not None
            SCREAMING_SNAKE_CASE__       							= te.Linear(
                module.in_features   ,       module.out_features   ,       bias=lowerCAmelCase_   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            if has_bias:
                SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       nn.LayerNorm  ) and to_transformer_engine and _convert_ln:
            SCREAMING_SNAKE_CASE__       							= te.LayerNorm(module.normalized_shape[0]   ,       eps=module.eps   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       te.Linear  ) and not to_transformer_engine and _convert_linear:
            SCREAMING_SNAKE_CASE__       							= module.bias is not None
            SCREAMING_SNAKE_CASE__       							= nn.Linear(
                module.in_features   ,       module.out_features   ,       bias=lowerCAmelCase_   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            if has_bias:
                SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        elif isinstance(lowerCAmelCase_   ,       te.LayerNorm  ) and not to_transformer_engine and _convert_ln:
            SCREAMING_SNAKE_CASE__       							= nn.LayerNorm(module.normalized_shape[0]   ,       eps=module.eps   ,       params_dtype=module.weight.dtype  )
            SCREAMING_SNAKE_CASE__       							= module.weight.data.clone()
            SCREAMING_SNAKE_CASE__       							= module.bias.data.clone()

            setattr(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        else:
            convert_model(
                lowerCAmelCase_   ,       to_transformer_engine=lowerCAmelCase_   ,       _convert_linear=_convert_linear   ,       _convert_ln=_convert_ln   ,       )



def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    if not is_fpa_available():
        raise ImportError('''Using `has_transformer_engine_layers` requires transformer_engine to be installed.'''  )
    for m in model.modules():
        if isinstance(lowerCAmelCase_   ,       (te.LayerNorm, te.Linear)  ):
            return True
    return False
