



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


__UpperCAmelCase    	=  [nn.LayerNorm]

__UpperCAmelCase    	=  logging.get_logger(__name__)

__UpperCAmelCase    	=  version.parse(version.parse(torch.__version__).base_version)

__UpperCAmelCase    	=  parsed_torch_version_base >= version.parse('''2.0''')
__UpperCAmelCase    	=  parsed_torch_version_base >= version.parse('''1.12''')
__UpperCAmelCase    	=  parsed_torch_version_base >= version.parse('''1.11''')
__UpperCAmelCase    	=  parsed_torch_version_base < version.parse('''1.11''')





def     __lowerCamelCase							(   __magic_name__						:	Optional[Any]  ,			__magic_name__						:	Any  ,			__magic_name__						:	Optional[int]  ,			__magic_name__						:	Any  ,			self						:	str    ):
							from torch import _softmax_backward_data

							if is_torch_less_than_1_11:
														return _softmax_backward_data(__magic_name__  ,			__magic_name__  ,			parent.dim  ,			self    )
							else:
														return _softmax_backward_data(__magic_name__  ,			__magic_name__  ,			parent.dim  ,			self.dtype    )





def     __lowerCamelCase							(   __magic_name__						:	nn.Linear  ,			__magic_name__						:	torch.LongTensor  ,			__magic_name__						:	int = 0    ):
							a__:							List[str]									=index.to(layer.weight.device    )
							a__:							Dict									=layer.weight.index_select(__magic_name__  ,			__magic_name__    ).clone().detach()
							if layer.bias is not None:
														if dim == 1:
																					a__:							Optional[Any]									=layer.bias.clone().detach()
														else:
																					a__:							Optional[Any]									=layer.bias[index].clone().detach()
							a__:							str									=list(layer.weight.size()    )
							a__:							Dict									=len(__magic_name__    )
							a__:							Dict									=nn.Linear(new_size[1]  ,			new_size[0]  ,			bias=layer.bias is not None    ).to(layer.weight.device    )
							a__:							Optional[int]									=False
							new_layer.weight.copy_(W.contiguous()    )
							a__:							Union[str, Any]									=True
							if layer.bias is not None:
														a__:							Any									=False
														new_layer.bias.copy_(b.contiguous()    )
														a__:							Dict									=True
							return new_layer


class 						lowerCamelCase__			(		nn.Module ):


						def __init__(			self    :	Dict   ,							_a    :	Tuple   ,							_a    :	List[Any]      ):
													super().__init__()
													a__:							Union[str, Any]									=nf
													a__:							str									=nn.Parameter(torch.empty(_a   ,							_a      )      )
													a__:							List[str]									=nn.Parameter(torch.zeros(_a      )      )
													nn.init.normal_(self.weight   ,							std=0.0_2      )







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	Dict      ):
													a__:							List[Any]									=x.size()[:-1] + (self.nf,)
													a__:							Dict									=torch.addmm(self.bias   ,							x.view(-1   ,							x.size(-1      )      )   ,							self.weight      )
													a__:							int									=x.view(_a      )
													return x





def     __lowerCamelCase							(   __magic_name__						:	ConvaD  ,			__magic_name__						:	torch.LongTensor  ,			__magic_name__						:	int = 1    ):
							a__:							str									=index.to(layer.weight.device    )
							a__:							List[Any]									=layer.weight.index_select(__magic_name__  ,			__magic_name__    ).clone().detach()
							if dim == 0:
														a__:							Optional[Any]									=layer.bias.clone().detach()
							else:
														a__:							str									=layer.bias[index].clone().detach()
							a__:							Optional[Any]									=list(layer.weight.size()    )
							a__:							Optional[Any]									=len(__magic_name__    )
							a__:							List[str]									=ConvaD(new_size[1]  ,			new_size[0]    ).to(layer.weight.device    )
							a__:							List[str]									=False
							new_layer.weight.copy_(W.contiguous()    )
							a__:							str									=True
							a__:							Optional[Any]									=False
							new_layer.bias.copy_(b.contiguous()    )
							a__:							Optional[int]									=True
							return new_layer





def     __lowerCamelCase							(   __magic_name__						:	Union[nn.Linear, ConvaD]  ,			__magic_name__						:	torch.LongTensor  ,			__magic_name__						:	Optional[int] = None    ):
							if isinstance(__magic_name__  ,			nn.Linear    ):
														return prune_linear_layer(__magic_name__  ,			__magic_name__  ,			dim=0 if dim is None else dim    )
							elif isinstance(__magic_name__  ,			__magic_name__    ):
														return prune_convad_layer(__magic_name__  ,			__magic_name__  ,			dim=1 if dim is None else dim    )
							else:
														raise ValueError(F"Can't prune layer of class {layer.__class__}"    )





def     __lowerCamelCase							(   __magic_name__						:	Callable[..., torch.Tensor]  ,			__magic_name__						:	int  ,			__magic_name__						:	int  ,			*__magic_name__						:	Tuple    ):
							assert len(__magic_name__    ) > 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
							a__:							List[Any]									=len(inspect.signature(__magic_name__    ).parameters    )
							if num_args_in_forward_chunk_fn != len(__magic_name__    ):
														raise ValueError(
														    F"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__magic_name__    )} input "
														    "tensors are given"    )

							if chunk_size > 0:
														a__:							Dict									=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}"    )

														a__:							Tuple									=input_tensors[0].shape[chunk_dim] // chunk_size

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

							return forward_fn(*__magic_name__    )





def     __lowerCamelCase							(   __magic_name__						:	List[int]  ,			__magic_name__						:	int  ,			__magic_name__						:	int  ,			__magic_name__						:	Set[int]    ):
							a__:							Any									=torch.ones(__magic_name__  ,			__magic_name__    )
							a__:							int									=set(__magic_name__    ) - 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
														a__:							List[Any]									=head - sum(1 if h < head else 0 for h in already_pruned_heads    )
														a__:							Any									=0
							a__:							str									=mask.view(-1    ).contiguous().eq(1    )
							a__:							torch.LongTensor									=torch.arange(len(__magic_name__    )    )[mask].long()
							return heads, index





def     __lowerCamelCase							(   *__magic_name__						:	Union[torch.Tensor, List[torch.Tensor]]  ,			__magic_name__						:	Optional[str] = None    ):
							return torch.meshgrid(*__magic_name__  ,			indexing=__magic_name__    )







def     __lowerCamelCase							(   __magic_name__						:	torch.Tensor    ):
							return tensor.device, storage_ptr(__magic_name__    ), storage_size(__magic_name__    )










from typing import Callable





def     __lowerCamelCase							(   __magic_name__						:	Callable    ):

							def wrapper(__magic_name__						:	Optional[Any]    ):
														a__:							List[Any]									=function_with_docstring.__doc__
														return target_function

							return wrapper





