
"""simple docstring"""







lowerCamelCase__     =						float("""inf""")




class 					A__			:

		def __init__( self			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     :		str		     = [0] * n
			__lowerCAmelCase     :		str		     = [0] * n
			__lowerCAmelCase     :		str		     = [0] * n
			__lowerCAmelCase     :		int		     = [[] for _ in range(lowerCamelCase_					)]

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=0					):
			self.adj[a].append([b, len(self.adj[b]					), c, 0]					)
			self.adj[b].append([a, len(self.adj[a]					) - 1, rcap, 0]					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			if vertex == sink or not flow:
				return flow

			for i in range(self.ptr[vertex]			,						len(self.adj[vertex]					)					):
				__lowerCAmelCase     :		str		     = self.adj[vertex][i]
				if self.lvl[e[0]] == self.lvl[vertex] + 1:
					__lowerCAmelCase     :		Optional[int]		     = self.depth_first_search(e[0]			,						lowerCamelCase_			,						min(lowerCamelCase_			,						e[2] - e[3]					)					)
					if p:
						self.adj[vertex][i][3] += p
						self.adj[e[0]][e[1]][3] -= p
						return p
				__lowerCAmelCase     :		Any		     = self.ptr[vertex] + 1
			return 0







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					):
			__lowerCAmelCase     ,  __lowerCAmelCase     :		str		     = 0, source
			for l in range(31					):  # noqa: E741  l = 30 maybe faster for random data
				while True:
					__lowerCAmelCase     ,  __lowerCAmelCase     :		Any		     = [0] * len(self.q					), [0] * len(self.q					)
					__lowerCAmelCase     ,  __lowerCAmelCase     ,  __lowerCAmelCase     :		Tuple		     = 0, 1, 1
					while qi < qe and not self.lvl[sink]:
						__lowerCAmelCase     :		int		     = self.q[qi]
						qi += 1
						for e in self.adj[v]:
							if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
								__lowerCAmelCase     :		str		     = e[0]
								qe += 1
								__lowerCAmelCase     :		Optional[Any]		     = self.lvl[v] + 1

					__lowerCAmelCase     :		str		     = self.depth_first_search(lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_					)
					while p:
						flow += p
						__lowerCAmelCase     :		Tuple		     = self.depth_first_search(lowerCamelCase_			,						lowerCamelCase_			,						lowerCamelCase_					)

					if not self.lvl[sink]:
						break

			return flow



lowerCamelCase__     =						Dinic(10)
lowerCamelCase__     =						0
lowerCamelCase__     =						9
for vertex in range(1, 5):
							graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
							graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
							graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))
"""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       			=      [nn.LayerNorm]

__A       			=      logging.get_logger(__name__)

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

__A       			=      parsed_torch_version_base >= version.parse("""2.0""")
__A       			=      parsed_torch_version_base >= version.parse("""1.12""")
__A       			=      parsed_torch_version_base >= version.parse("""1.11""")
__A       			=      parsed_torch_version_base < version.parse("""1.11""")

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






class 					a					(				nn.Module							):



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



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    Optional[Any]     )      -> Union[str, Any]:
      __a			      =						x.size()[:-1] + (self.nf,)
      __a			      =						torch.addmm(self.bias						,  x.view(-1						,  x.size(-1     )     )						,  self.weight     )
      __a			      =						x.view(lowerCamelCase_     )
      return x

def 						UpperCamelCase				(	_lowerCAmelCase   :							ConvaD				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							int = 1						):
    __a			      =						index.to(layer.weight.device						)
    __a			      =						layer.weight.index_select(_lowerCAmelCase				, _lowerCAmelCase						).clone().detach()
    if dim == 0:
        __a			      =						layer.bias.clone().detach()
    else:
        __a			      =						layer.bias[index].clone().detach()
    __a			      =						list(layer.weight.size()						)
    __a			      =						len(_lowerCAmelCase						)
    __a			      =						ConvaD(new_size[1]				, new_size[0]						).to(layer.weight.device						)
    __a			      =						False
    new_layer.weight.copy_(W.contiguous()						)
    __a			      =						True
    __a			      =						False
    new_layer.bias.copy_(b.contiguous()						)
    __a			      =						True
    return new_layer

def 						UpperCamelCase				(	_lowerCAmelCase   :							Union[nn.Linear, ConvaD]				, _lowerCAmelCase   :							torch.LongTensor				, _lowerCAmelCase   :							Optional[int] = None						):
    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]						):
    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
    __a			      =						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:
        __a			      =						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			      =						input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        __a			      =						tuple(input_tensor.chunk(_lowerCAmelCase				, dim=_lowerCAmelCase						) for input_tensor in input_tensors						)
        # apply forward fn to every tuple
        __a			      =						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]						):
    __a			      =						torch.ones(_lowerCAmelCase				, _lowerCAmelCase						)
    __a			      =						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
        __a			      =						head - sum(1 if h < head else 0 for h in already_pruned_heads						)
        __a			      =						0
    __a			      =						mask.view(-1						).contiguous().eq(1						)
    __a			      =						torch.arange(len(_lowerCAmelCase						)						)[mask].long()
    return heads, index

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






def 						UpperCamelCase				(	_lowerCAmelCase   :							torch.Tensor						):
    return tensor.device, storage_ptr(_lowerCAmelCase						), storage_size(_lowerCAmelCase						)
