

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


SCREAMING_SNAKE_CASE						:Dict      =			[nn.LayerNorm]

SCREAMING_SNAKE_CASE						:List[Any]      =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:int      =			version.parse(version.parse(torch.__version__).base_version)

SCREAMING_SNAKE_CASE						:List[str]      =			parsed_torch_version_base >= version.parse("""2.0""")
SCREAMING_SNAKE_CASE						:Any      =			parsed_torch_version_base >= version.parse("""1.12""")
SCREAMING_SNAKE_CASE						:Tuple      =			parsed_torch_version_base >= version.parse("""1.11""")
SCREAMING_SNAKE_CASE						:Optional[int]      =			parsed_torch_version_base < version.parse("""1.11""")




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		self       )->		Dict:



    """simple docstring"""






    from torch import _softmax_backward_data

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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 0       )->		nn.Linear:



    """simple docstring"""






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






class 		__magic_name__ (				nn.Module							):






     def __init__(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         super().__init__()
         UpperCamelCase_          =		nf
         UpperCamelCase_          =		nn.Parameter(torch.empty(_lowercase						,		_lowercase     )     )
         UpperCamelCase_          =		nn.Parameter(torch.zeros(_lowercase     )     )
         nn.init.normal_(self.weight						,		std=0.02     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			List[str]:
         UpperCamelCase_          =		x.size()[:-1] + (self.nf,)
         UpperCamelCase_          =		torch.addmm(self.bias						,		x.view(-1						,		x.size(-1     )     )						,		self.weight     )
         UpperCamelCase_          =		x.view(_lowercase     )
         return x




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       )->		ConvaD:



    """simple docstring"""






    UpperCamelCase_          =		index.to(layer.weight.device       )
    UpperCamelCase_          =		layer.weight.index_select(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ).clone().detach()
    if dim == 0:
        UpperCamelCase_          =		layer.bias.clone().detach()
    else:
        UpperCamelCase_          =		layer.bias[index].clone().detach()
    UpperCamelCase_          =		list(layer.weight.size()       )
    UpperCamelCase_          =		len(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		ConvaD(new_size[1]       ,		new_size[0]       ).to(layer.weight.device       )
    UpperCamelCase_          =		False
    new_layer.weight.copy_(W.contiguous()       )
    UpperCamelCase_          =		True
    UpperCamelCase_          =		False
    new_layer.bias.copy_(b.contiguous()       )
    UpperCamelCase_          =		True
    return new_layer




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None       )->		Union[nn.Linear, ConvaD]:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_       ,		nn.Linear       ):
        return prune_linear_layer(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		dim=0 if dim is None else dim       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return prune_convad_layer(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		dim=1 if dim is None else dim       )
    else:
        raise ValueError(f"Can't prune layer of class {layer.__class__}"       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		*SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    assert len(SCREAMING_SNAKE_CASE_       ) > 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_          =		len(inspect.signature(SCREAMING_SNAKE_CASE_       ).parameters       )
    if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE_       ):
        raise ValueError(
            f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE_       )} input "
            "tensors are given"       )

    if chunk_size > 0:
        UpperCamelCase_          =		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_          =		input_tensors[0].shape[chunk_dim] // chunk_size

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

    return forward_fn(*SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Tuple[Set[int], torch.LongTensor]:



    """simple docstring"""






    UpperCamelCase_          =		torch.ones(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		set(SCREAMING_SNAKE_CASE_       ) - 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_          =		head - sum(1 if h < head else 0 for h in already_pruned_heads       )
        UpperCamelCase_          =		0
    UpperCamelCase_          =		mask.view(-1       ).contiguous().eq(1       )
    UpperCamelCase_          =		torch.arange(len(SCREAMING_SNAKE_CASE_       )       )[mask].long()
    return heads, index




def        lowerCAmelCase(	*SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None       )->		Tuple[torch.Tensor, ...]:



    """simple docstring"""






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




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		Tuple[torch.device, int, int]:



    """simple docstring"""






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



from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    if not inplace:
        UpperCamelCase_          =		ma + ma
    else:
        ma += ma

    return ma




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		-1 * len(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		list(range(len(tensor.shape[:zero_index]       )       )       )
    return tensor.permute(first_inds + [zero_index + i for i in inds]       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    return t.reshape(t.shape[:-no_dims] + (-1,)       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1E-4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		mask.expand(*value.shape       )
    return torch.sum(mask * value       ,		dim=SCREAMING_SNAKE_CASE_       ) / (eps + torch.sum(SCREAMING_SNAKE_CASE_       ,		dim=SCREAMING_SNAKE_CASE_       ))




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 2.3125       ,		SCREAMING_SNAKE_CASE_ = 21.6875       ,		SCREAMING_SNAKE_CASE_ = 6_4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		torch.linspace(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		no_bins - 1       ,		device=pts.device       )
    UpperCamelCase_          =		torch.sqrt(torch.sum((pts.unsqueeze(-2       ) - pts.unsqueeze(-3       )) ** 2       ,		dim=-1       )       )
    return torch.bucketize(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    UpperCamelCase_          =		dicts[0]
    UpperCamelCase_          =		{}
    for k, v in first.items():
        UpperCamelCase_          =		[d[k] for d in dicts]
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_multimap(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		fn(SCREAMING_SNAKE_CASE_       )

    return new_dict




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		v_bins.view(((1,) * len(x.shape       )) + (len(SCREAMING_SNAKE_CASE_       ),)       )
    UpperCamelCase_          =		x[..., None] - reshaped_bins
    UpperCamelCase_          =		torch.argmin(torch.abs(SCREAMING_SNAKE_CASE_       )       ,		dim=-1       )
    return nn.functional.one_hot(SCREAMING_SNAKE_CASE_       ,		num_classes=len(SCREAMING_SNAKE_CASE_       )       ).float()




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 0       ,		SCREAMING_SNAKE_CASE_ = 0       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		[]
    for i, s in enumerate(data.shape[:no_batch_dims]       ):
        UpperCamelCase_          =		torch.arange(SCREAMING_SNAKE_CASE_       )
        UpperCamelCase_          =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape       ) - i - 1)))       )
        ranges.append(SCREAMING_SNAKE_CASE_       )

    UpperCamelCase_          =		[slice(SCREAMING_SNAKE_CASE_       ) for _ in range(len(data.shape       ) - no_batch_dims       )]
    UpperCamelCase_          =		inds
    ranges.extend(SCREAMING_SNAKE_CASE_       )
    # Matt note: Editing this to get around the behaviour of using a list as an array index changing
    # in recent Numpy versions
    return data[tuple(SCREAMING_SNAKE_CASE_       )]


SCREAMING_SNAKE_CASE						:Dict      =			TypeVar("""T""")




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Dict[Any, Union[dict, list, tuple, Any]]:



    """simple docstring"""






    UpperCamelCase_          =		{}
    for k, v in dic.items():
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    return new_dict




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple:



    """simple docstring"""






    ...




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return [tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree]
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return tuple(tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return fn(SCREAMING_SNAKE_CASE_       )
    else:
        print(type(SCREAMING_SNAKE_CASE_       )       )
        raise ValueError("Not supported"       )
SCREAMING_SNAKE_CASE						:Dict      =			partial(tree_map, leaf_type=torch.Tensor)
