




"""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	:      Any   =							[nn.LayerNorm]

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

_lowerCamelCase	:      Union[str, Any]   =							version.parse(version.parse(torch.__version__).base_version)

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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   self						):




	"""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_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = 0						):




	"""simple docstring"""






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

class 							lowercase   (     nn.Module):







				def __init__(				self :       Tuple   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       Tuple							):



					"""simple docstring"""

					super().__init__()
					A_	:					str						 =		nf
					A_	:					Dict						 =		nn.Parameter(torch.empty(_lowerCamelCase   ,  _lowerCamelCase							)							)
					A_	:					str						 =		nn.Parameter(torch.zeros(_lowerCamelCase							)							)
					nn.init.normal_(self.weight   ,  std=0.02							)







				def        a_	(				self :       int   ,  _lowerCamelCase :       Any							):



					"""simple docstring"""

					A_	:					int						 =		x.size()[:-1] + (self.nf,)
					A_	:					str						 =		torch.addmm(self.bias   ,  x.view(-1   ,  x.size(-1							)							)   ,  self.weight							)
					A_	:					Optional[Any]						 =		x.view(_lowerCamelCase							)
					return x

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = 1						):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase = None						):




	"""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_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   *_UpperCAmelCase						):




	"""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
	A_	:					Optional[Any]						 =		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:
		A_	:					List[str]						 =		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_	:					Dict						 =		input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		A_	:					List[Any]						 =		tuple(input_tensor.chunk(_UpperCAmelCase	,   dim=_UpperCAmelCase						) for input_tensor in input_tensors						)
		# apply forward fn to every tuple
		A_	:					Optional[Any]						 =		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_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Union[str, Any]						 =		torch.ones(_UpperCAmelCase	,   _UpperCAmelCase						)
	A_	:					int						 =		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
		A_	:					Union[str, Any]						 =		head - sum(1 if h < head else 0 for h in already_pruned_heads						)
		A_	:					Optional[Any]						 =		0
	A_	:					str						 =		mask.view(-1						).contiguous().eq(1						)
	A_	:					torch.LongTensor						 =		torch.arange(len(_UpperCAmelCase						)						)[mask].long()
	return heads, index

def 		lowercase_   (		*_UpperCAmelCase	,   _UpperCAmelCase = None						):




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






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











"""simple docstring"""

# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class 							lowercase   :







				def __init__(				self :       Optional[int]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		key
					A_	:					str						 =		freq







				def __str__(				self :       Optional[Any]							):



					"""simple docstring"""

					return F"""Node(key={self.key}, freq={self.freq})"""

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if i > j or i < 0 or j > len(_UpperCAmelCase						) - 1:
		return

	A_	:					Union[str, Any]						 =		root[i][j]
	if parent == -1:  # root does not have a parent
		print(f"""{key[node]} is the root of the binary search tree."""						)
	elif is_left:
		print(f"""{key[node]} is the left child of key {parent}."""						)
	else:
		print(f"""{key[node]} is the right child of key {parent}."""						)

	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   node - 1	,   key[node]	,   _UpperCAmelCase						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   node + 1	,   _UpperCAmelCase	,   key[node]	,   _UpperCAmelCase						)

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	nodes.sort(key=lambda _UpperCAmelCase						: node.key						)

	A_	:					Any						 =		len(_UpperCAmelCase						)

	A_	:					int						 =		[nodes[i].key for i in range(_UpperCAmelCase						)]
	A_	:					List[str]						 =		[nodes[i].freq for i in range(_UpperCAmelCase						)]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	A_	:					List[str]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	A_	:					Optional[int]						 =		[[freqs[i] if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]
	# stores tree roots that will be used later for constructing binary search tree
	A_	:					List[Any]						 =		[[i if i == j else 0 for j in range(_UpperCAmelCase						)] for i in range(_UpperCAmelCase						)]

	for interval_length in range(2	,   n + 1						):
		for i in range(n - interval_length + 1						):
			A_	:					List[Any]						 =		i + interval_length - 1

			A_	:					List[Any]						 =		sys.maxsize  # set the value to "infinity"
			A_	:					Union[str, Any]						 =		total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]	,   root[i + 1][j] + 1						):  # r is a temporal root
				A_	:					Tuple						 =		dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				A_	:					List[str]						 =		dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				A_	:					Optional[int]						 =		left + total[i][j] + right

				if dp[i][j] > cost:
					A_	:					Dict						 =		cost
					A_	:					List[str]						 =		r

	print('''Binary search tree nodes:'''						)
	for node in nodes:
		print(_UpperCAmelCase						)

	print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""						)
	print_binary_search_tree(_UpperCAmelCase	,   _UpperCAmelCase	,   0	,   n - 1	,   -1	,   _UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		[Node(_UpperCAmelCase	,   randint(1	,   50						)						) for i in range(10	,   0	,   -1						)]
	find_optimal_binary_search_tree(_UpperCAmelCase						)


if __name__ == "__main__":
		main()





