
"""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


SCREAMING_SNAKE_CASE_         =  [nn.LayerNorm]

SCREAMING_SNAKE_CASE_         =  logging.get_logger(__name__)

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

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




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Any	,			self			:   str	)	-> Any:






    """simple docstring"""




    from torch import _softmax_backward_data

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




def       lowercase__  (   lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   int	,			lowerCAmelCase			:   Union[str, Any] = 0	)	-> Optional[int]:






    """simple docstring"""




    UpperCAmelCase       =   index.to(layer.weight.device	)
    UpperCAmelCase       =   layer.weight.index_select(__UpperCamelCase	,			__UpperCamelCase	).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(__UpperCamelCase	)
    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        _UpperCAmelCase							(						nn.Module							):




      def __init__(							self				,    lowercase_				,    lowercase_							)  ->   List[Any]:
          super().__init__()
          UpperCAmelCase       =   nf
          UpperCAmelCase       =   nn.Parameter(torch.empty(_UpperCamelCase				,    _UpperCamelCase							)							)
          UpperCAmelCase       =   nn.Parameter(torch.zeros(_UpperCamelCase							)							)
          nn.init.normal_(self.weight				,    std=0.0_2							)




      def       a_ (							self				,    lowercase_							)  ->   Tuple:
          UpperCAmelCase       =   x.size()[:-1] + (self.nf,)
          UpperCAmelCase       =   torch.addmm(self.bias				,    x.view(-1				,    x.size(-1							)							)				,    self.weight							)
          UpperCAmelCase       =   x.view(_UpperCamelCase							)
          return x




def       lowercase__  (   lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   int	,			lowerCAmelCase			:   str = 1	)	-> Any:






    """simple docstring"""




    UpperCAmelCase       =   index.to(layer.weight.device	)
    UpperCAmelCase       =   layer.weight.index_select(__UpperCamelCase	,			__UpperCamelCase	).clone().detach()
    if dim == 0:
        UpperCAmelCase       =   layer.bias.clone().detach()
    else:
        UpperCAmelCase       =   layer.bias[index].clone().detach()
    UpperCAmelCase       =   list(layer.weight.size()	)
    UpperCAmelCase       =   len(__UpperCamelCase	)
    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       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   str = None	)	-> Optional[int]:






    """simple docstring"""




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




def       lowercase__  (   lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   Dict	,			*lowerCAmelCase			:   Any	)	-> Any:






    """simple docstring"""




    assert len(__UpperCamelCase	) > 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(__UpperCamelCase	).parameters	)
    if num_args_in_forward_chunk_fn != len(__UpperCamelCase	):
        raise ValueError(
            F"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__UpperCamelCase	)} 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(__UpperCamelCase	,			dim=__UpperCamelCase	) for input_tensor in input_tensors	)
        # apply forward fn to every tuple
        UpperCAmelCase       =   tuple(forward_fn(*__UpperCamelCase	) for input_tensors_chunk in zip(*__UpperCamelCase	)	)
        # concatenate output at same dimension
        return torch.cat(__UpperCamelCase	,			dim=__UpperCamelCase	)

    return forward_fn(*__UpperCamelCase	)




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   str	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   torch.ones(__UpperCamelCase	,			__UpperCamelCase	)
    UpperCAmelCase       =   set(__UpperCamelCase	) - 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(__UpperCamelCase	)	)[mask].long()
    return heads, index




def       lowercase__  (   *lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Tuple = None	)	-> str:






    """simple docstring"""




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







def       lowercase__  (   lowerCAmelCase			:   Optional[int]	)	-> Any:






    """simple docstring"""




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





"""simple docstring"""

import heapq




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   int	,			lowerCAmelCase			:   Optional[Any]	)	-> Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase       =   [(0, start)]  # cost from start node,end node
    UpperCAmelCase       =   set()
    while heap:
        ((UpperCAmelCase)     ,							(UpperCAmelCase))       =   heapq.heappop(lowerCAmelCase	)
        if u in visited:
            continue
        visited.add(lowerCAmelCase	)
        if u == end:
            return cost
        for v, c in graph[u]:
            if v in visited:
                continue
            UpperCAmelCase       =   cost + c
            heapq.heappush(lowerCAmelCase	,			(next_item, v)	)
    return -1


SCREAMING_SNAKE_CASE_         =  {
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
SCREAMING_SNAKE_CASE_         =  {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
SCREAMING_SNAKE_CASE_         =  {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

SCREAMING_SNAKE_CASE_         =  dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

SCREAMING_SNAKE_CASE_         =  dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

SCREAMING_SNAKE_CASE_         =  dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
   import doctest

   doctest.testmod()



