

"""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


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

lowerCamelCase__				:  List[str]													=  logging.get_logger(__name__)

lowerCamelCase__				:  Optional[Any]													=  version.parse(version.parse(torch.__version__).base_version)

lowerCamelCase__				:  Any													=  parsed_torch_version_base >= version.parse('''2.0''')
lowerCamelCase__				:  Optional[Any]													=  parsed_torch_version_base >= version.parse('''1.12''')
lowerCamelCase__				:  Any													=  parsed_torch_version_base >= version.parse('''1.11''')
lowerCamelCase__				:  Union[str, Any]													=  parsed_torch_version_base < version.parse('''1.11''')
def       UpperCamelCase					(			_lowerCAmelCase     :						str,  _lowerCAmelCase     :						List[Any],  _lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						List[str],  self     :						Dict )				->			Optional[int]:
	from torch import _softmax_backward_data

	if is_torch_less_than_1_11:
		return _softmax_backward_data(_lowerCAmelCase,  _lowerCAmelCase,  parent.dim,  self )
	else:
		return _softmax_backward_data(_lowerCAmelCase,  _lowerCAmelCase,  parent.dim,  self.dtype )
def       UpperCamelCase					(			_lowerCAmelCase     :						nn.Linear,  _lowerCAmelCase     :						torch.LongTensor,  _lowerCAmelCase     :						int = 0 )				->			nn.Linear:
	_UpperCAmelCase    :							Any									=					index.to(layer.weight.device )
	_UpperCAmelCase    :							List[Any]									=					layer.weight.index_select(_lowerCAmelCase,  _lowerCAmelCase ).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			_UpperCAmelCase    :							List[str]									=					layer.bias.clone().detach()
		else:
			_UpperCAmelCase    :							Optional[Any]									=					layer.bias[index].clone().detach()
	_UpperCAmelCase    :							Any									=					list(layer.weight.size() )
	_UpperCAmelCase    :							Optional[int]									=					len(_lowerCAmelCase )
	_UpperCAmelCase    :							Any									=					nn.Linear(new_size[1],  new_size[0],  bias=layer.bias is not None ).to(layer.weight.device )
	_UpperCAmelCase    :							Optional[int]									=					False
	new_layer.weight.copy_(W.contiguous() )
	_UpperCAmelCase    :							int									=					True
	if layer.bias is not None:
		_UpperCAmelCase    :							Union[str, Any]									=					False
		new_layer.bias.copy_(b.contiguous() )
		_UpperCAmelCase    :							Optional[int]									=					True
	return new_layer






class     _UpperCAmelCase			(		nn.Module):
			def __init__(		self      ,		_A      ,		_A     )    ->    Union[str, Any]:






				'''simple docstring'''







				super().__init__()
				_UpperCAmelCase    :							Optional[Any]									=					nf
				_UpperCAmelCase    :							Union[str, Any]									=					nn.Parameter(torch.empty(_A      ,		_A     )     )
				_UpperCAmelCase    :							Any									=					nn.Parameter(torch.zeros(_A     )     )
				nn.init.normal_(self.weight      ,		std=0.02     )
			def 							__snake_case (		self      ,		_A     )    ->    Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase    :							List[str]									=					x.size()[:-1] + (self.nf,)
				_UpperCAmelCase    :							Optional[Any]									=					torch.addmm(self.bias      ,		x.view(-1      ,		x.size(-1     )     )      ,		self.weight     )
				_UpperCAmelCase    :							Optional[int]									=					x.view(_A     )
				return x
def       UpperCamelCase					(			_lowerCAmelCase     :						ConvaD,  _lowerCAmelCase     :						torch.LongTensor,  _lowerCAmelCase     :						int = 1 )				->			ConvaD:
	_UpperCAmelCase    :							Tuple									=					index.to(layer.weight.device )
	_UpperCAmelCase    :							Tuple									=					layer.weight.index_select(_lowerCAmelCase,  _lowerCAmelCase ).clone().detach()
	if dim == 0:
		_UpperCAmelCase    :							Tuple									=					layer.bias.clone().detach()
	else:
		_UpperCAmelCase    :							Optional[int]									=					layer.bias[index].clone().detach()
	_UpperCAmelCase    :							Optional[Any]									=					list(layer.weight.size() )
	_UpperCAmelCase    :							List[Any]									=					len(_lowerCAmelCase )
	_UpperCAmelCase    :							int									=					ConvaD(new_size[1],  new_size[0] ).to(layer.weight.device )
	_UpperCAmelCase    :							Optional[Any]									=					False
	new_layer.weight.copy_(W.contiguous() )
	_UpperCAmelCase    :							int									=					True
	_UpperCAmelCase    :							Tuple									=					False
	new_layer.bias.copy_(b.contiguous() )
	_UpperCAmelCase    :							List[Any]									=					True
	return new_layer
def       UpperCamelCase					(			_lowerCAmelCase     :						Union[nn.Linear, ConvaD],  _lowerCAmelCase     :						torch.LongTensor,  _lowerCAmelCase     :						Optional[int] = None )				->			Union[nn.Linear, ConvaD]:
	if isinstance(_lowerCAmelCase,  nn.Linear ):
		return prune_linear_layer(_lowerCAmelCase,  _lowerCAmelCase,  dim=0 if dim is None else dim )
	elif isinstance(_lowerCAmelCase,  _lowerCAmelCase ):
		return prune_convad_layer(_lowerCAmelCase,  _lowerCAmelCase,  dim=1 if dim is None else dim )
	else:
		raise ValueError(f'''Can\'t prune layer of class {layer.__class__}''' )
def       UpperCamelCase					(			_lowerCAmelCase     :						Callable[..., torch.Tensor],  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						int,  *_lowerCAmelCase     :						List[Any] )				->			torch.Tensor:
	assert len(_lowerCAmelCase ) > 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
	_UpperCAmelCase    :							Dict									=					len(inspect.signature(_lowerCAmelCase ).parameters )
	if num_args_in_forward_chunk_fn != len(_lowerCAmelCase ):
		raise ValueError(
		    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(_lowerCAmelCase )} input '''
		    """tensors are given""" )

	if chunk_size > 0:
		_UpperCAmelCase    :							Optional[Any]									=					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}''' )

		_UpperCAmelCase    :							List[str]									=					input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		_UpperCAmelCase    :							List[str]									=					tuple(input_tensor.chunk(_lowerCAmelCase,  dim=_lowerCAmelCase ) for input_tensor in input_tensors )
		# apply forward fn to every tuple
		_UpperCAmelCase    :							Tuple									=					tuple(forward_fn(*_lowerCAmelCase ) for input_tensors_chunk in zip(*_lowerCAmelCase ) )
		# concatenate output at same dimension
		return torch.cat(_lowerCAmelCase,  dim=_lowerCAmelCase )

	return forward_fn(*_lowerCAmelCase )
def       UpperCamelCase					(			_lowerCAmelCase     :						List[int],  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						Set[int] )				->			Tuple[Set[int], torch.LongTensor]:
	_UpperCAmelCase    :							Any									=					torch.ones(_lowerCAmelCase,  _lowerCAmelCase )
	_UpperCAmelCase    :							List[str]									=					set(_lowerCAmelCase ) - 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
		_UpperCAmelCase    :							Any									=					head - sum(1 if h < head else 0 for h in already_pruned_heads )
		_UpperCAmelCase    :							List[Any]									=					0
	_UpperCAmelCase    :							List[str]									=					mask.view(-1 ).contiguous().eq(1 )
	_UpperCAmelCase    :							torch.LongTensor									=					torch.arange(len(_lowerCAmelCase ) )[mask].long()
	return heads, index
def       UpperCamelCase					(			*_lowerCAmelCase     :						Union[torch.Tensor, List[torch.Tensor]],  _lowerCAmelCase     :						Optional[str] = None )				->			Tuple[torch.Tensor, ...]:
	return torch.meshgrid(*_lowerCAmelCase,  indexing=_lowerCAmelCase )





def       UpperCamelCase					(			_lowerCAmelCase     :						torch.Tensor )				->			Tuple[torch.device, int, int]:
	return tensor.device, storage_ptr(_lowerCAmelCase ), storage_size(_lowerCAmelCase )








"""simple docstring"""






import safs






class     _UpperCAmelCase			(		safs.SaFileSystem):
			pass





