




'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


A				: Union[str, Any]					      =			[nn.LayerNorm]

A				: Any					      =			logging.get_logger(__name__)

A				: Any					      =			version.parse(version.parse(torch.__version__).base_version)

A				: Optional[Any]					      =			parsed_torch_version_base >= version.parse("""2.0""")
A				: List[Any]					      =			parsed_torch_version_base >= version.parse("""1.12""")
A				: Optional[int]					      =			parsed_torch_version_base >= version.parse("""1.11""")
A				: Tuple					      =			parsed_torch_version_base < version.parse("""1.11""")
def 							snake_case_    (  a__						:      Optional[int]       ,a__						:      List[str]       ,a__						:      Dict       ,a__						:      Dict       ,self						:      List[Any]     ):



    """simple docstring"""




    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(a__       ,a__       ,parent.dim       ,self     )
    else:
        return _softmax_backward_data(a__       ,a__       ,parent.dim       ,self.dtype     )
def 							snake_case_    (  a__						:      nn.Linear       ,a__						:      torch.LongTensor       ,a__						:      int = 0     ):



    """simple docstring"""




    __lowercase     							=		index.to(layer.weight.device     )
    __lowercase     							=		layer.weight.index_select(a__       ,a__     ).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            __lowercase     							=		layer.bias.clone().detach()
        else:
            __lowercase     							=		layer.bias[index].clone().detach()
    __lowercase     							=		list(layer.weight.size()     )
    __lowercase     							=		len(a__     )
    __lowercase     							=		nn.Linear(new_size[1]       ,new_size[0]       ,bias=layer.bias is not None     ).to(layer.weight.device     )
    __lowercase     							=		False
    new_layer.weight.copy_(W.contiguous()     )
    __lowercase     							=		True
    if layer.bias is not None:
        __lowercase     							=		False
        new_layer.bias.copy_(b.contiguous()     )
        __lowercase     							=		True
    return new_layer






class 			SCREAMING_SNAKE_CASE(							nn.Module	):

       def __init__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__	)       ->   Union[str, Any]:

           """simple docstring"""



           super().__init__()
           __lowercase     							=		nf
           __lowercase     							=		nn.Parameter(torch.empty(lowerCamelCase__   ,						lowerCamelCase__	)	)
           __lowercase     							=		nn.Parameter(torch.zeros(lowerCamelCase__	)	)
           nn.init.normal_(self.weight   ,						std=0.02	)




       def        snake_case__     (       self   ,						lowerCamelCase__	)       ->   Any:

           """simple docstring"""



           __lowercase     							=		x.size()[:-1] + (self.nf,)
           __lowercase     							=		torch.addmm(self.bias   ,						x.view(-1   ,						x.size(-1	)	)   ,						self.weight	)
           __lowercase     							=		x.view(lowerCamelCase__	)
           return x

def 							snake_case_    (  a__						:      ConvaD       ,a__						:      torch.LongTensor       ,a__						:      int = 1     ):



    """simple docstring"""




    __lowercase     							=		index.to(layer.weight.device     )
    __lowercase     							=		layer.weight.index_select(a__       ,a__     ).clone().detach()
    if dim == 0:
        __lowercase     							=		layer.bias.clone().detach()
    else:
        __lowercase     							=		layer.bias[index].clone().detach()
    __lowercase     							=		list(layer.weight.size()     )
    __lowercase     							=		len(a__     )
    __lowercase     							=		ConvaD(new_size[1]       ,new_size[0]     ).to(layer.weight.device     )
    __lowercase     							=		False
    new_layer.weight.copy_(W.contiguous()     )
    __lowercase     							=		True
    __lowercase     							=		False
    new_layer.bias.copy_(b.contiguous()     )
    __lowercase     							=		True
    return new_layer
def 							snake_case_    (  a__						:      Union[nn.Linear, ConvaD]       ,a__						:      torch.LongTensor       ,a__						:      Optional[int] = None     ):



    """simple docstring"""




    if isinstance(a__       ,nn.Linear     ):
        return prune_linear_layer(a__       ,a__       ,dim=0 if dim is None else dim     )
    elif isinstance(a__       ,a__     ):
        return prune_convad_layer(a__       ,a__       ,dim=1 if dim is None else dim     )
    else:
        raise ValueError(f'Can\'t prune layer of class {layer.__class__}'     )
def 							snake_case_    (  a__						:      Callable[..., torch.Tensor]       ,a__						:      int       ,a__						:      int       ,*a__						:      Dict     ):



    """simple docstring"""




    assert len(a__     ) > 0, f'{input_tensors} has to be a tuple/list of tensors'

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    __lowercase     							=		len(inspect.signature(a__     ).parameters     )
    if num_args_in_forward_chunk_fn != len(a__     ):
        raise ValueError(
            f'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(a__     )} input '
            """tensors are given"""     )

    if chunk_size > 0:
        __lowercase     							=		input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    f'All input tenors have to be of the same shape: {tensor_shape}, '
                    f'found shape {input_tensor.shape[chunk_dim]}'     )

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                f'The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '
                f'size {chunk_size}'     )

        __lowercase     							=		input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        __lowercase     							=		tuple(input_tensor.chunk(a__       ,dim=a__     ) for input_tensor in input_tensors     )
        # apply forward fn to every tuple
        __lowercase     							=		tuple(forward_fn(*a__     ) for input_tensors_chunk in zip(*a__     )     )
        # concatenate output at same dimension
        return torch.cat(a__       ,dim=a__     )

    return forward_fn(*a__     )
def 							snake_case_    (  a__						:      List[int]       ,a__						:      int       ,a__						:      int       ,a__						:      Set[int]     ):



    """simple docstring"""




    __lowercase     							=		torch.ones(a__       ,a__     )
    __lowercase     							=		set(a__     ) - already_pruned_heads  # Convert to set and remove already pruned heads
    for head in heads:
        # Compute how many pruned heads are before the head and move the index accordingly
        __lowercase     							=		head - sum(1 if h < head else 0 for h in already_pruned_heads     )
        __lowercase     							=		0
    __lowercase     							=		mask.view(-1     ).contiguous().eq(1     )
    __lowercase     							=		torch.arange(len(a__     )     )[mask].long()
    return heads, index
def 							snake_case_    (  *a__						:      Union[torch.Tensor, List[torch.Tensor]]       ,a__						:      Optional[str] = None     ):



    """simple docstring"""




    return torch.meshgrid(*a__       ,indexing=a__     )



def 							snake_case_    (  a__						:      torch.Tensor     ):



    """simple docstring"""




    return tensor.device, storage_ptr(a__     ), storage_size(a__     )










'''simple docstring'''
import numpy as np
def 							snake_case_    (  a__						:      Tuple     ):



    """simple docstring"""




    __lowercase							,__lowercase     							=		a.shape
    __lowercase     							=		min(a__       ,a__     )
    __lowercase     							=		np.eye(a__     )
    __lowercase     							=		a.copy()

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

        # construct the Householder matrix
        __lowercase     							=		np.eye(m - k     ) - 2.0 * v @ v.T
        # pad with ones and zeros as necessary
        __lowercase     							=		np.block([[np.eye(a__     ), np.zeros((k, m - k)     )], [np.zeros((m - k, k)     ), q_k]]     )

        __lowercase     							=		q @ q_k.T
        __lowercase     							=		q_k @ r

    return q, r


if __name__ == "__main__":
  import doctest

  doctest.testmod()




