





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__   (   UpperCamelCase__	:				Any				,			UpperCamelCase__	:				Dict				,			UpperCamelCase__	:				str				,			UpperCamelCase__	:				Any				,			self	:				List[Any]  )	->       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__   (   UpperCamelCase__	:				List[Any]				,			UpperCamelCase__	:				Tuple				,			UpperCamelCase__	:				List[str] = 0  )	->       nn.Linear:



					'''simple docstring'''




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




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






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





def   lowerCamelCase__   (   UpperCamelCase__	:				Union[str, Any]				,			UpperCamelCase__	:				Optional[int]				,			UpperCamelCase__	:				Union[str, Any] = 1  )	->       ConvaD:



					'''simple docstring'''




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





def   lowerCamelCase__   (   UpperCamelCase__	:				Dict				,			UpperCamelCase__	:				int				,			UpperCamelCase__	:				List[str] = 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__   (   UpperCamelCase__	:				Optional[int]				,			UpperCamelCase__	:				Union[str, Any]				,			UpperCamelCase__	:				int				,			*UpperCamelCase__	:				List[str]  )	->       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
					_snake_case													= 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:
										_snake_case													= 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}'''  )

										_snake_case													= input_tensors[0].shape[chunk_dim] // chunk_size

										# chunk input tensor into tuples
										_snake_case													= tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE_				,			dim=SCREAMING_SNAKE_CASE_  ) for input_tensor in input_tensors  )
										# apply forward fn to every tuple
										_snake_case													= 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__   (   UpperCamelCase__	:				Tuple				,			UpperCamelCase__	:				Optional[Any]				,			UpperCamelCase__	:				List[Any]				,			UpperCamelCase__	:				List[Any]  )	->       Tuple[Set[int], torch.LongTensor]:



					'''simple docstring'''




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





def   lowerCamelCase__   (   *UpperCamelCase__	:				Dict				,			UpperCamelCase__	:				Optional[int] = None  )	->       Tuple[torch.Tensor, ...]:



					'''simple docstring'''




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





def   lowerCamelCase__   (   UpperCamelCase__	:				Optional[int]  )	->       Tuple[torch.device, int, int]:



					'''simple docstring'''




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






class 		__magic_name__ :






     def __init__(							self     )->			int:
         UpperCamelCase_          =		[0]
         UpperCamelCase_          =		0






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			None:
         UpperCamelCase_          =		self.__heap[i]
         while i // 2 > 0:
             if self.__heap[i] > self.__heap[i // 2]:
                 UpperCamelCase_          =		self.__heap[i // 2]
                 UpperCamelCase_          =		temporary
             i //= 2






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			None:
         self.__heap.append(_lowercase     )
         self.__size += 1
         self.__swap_up(self.__size     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			None:
         while self.__size >= 2 * i:
             if 2 * i + 1 > self.__size:
                 UpperCamelCase_          =		2 * i
             else:
                 if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                     UpperCamelCase_          =		2 * i
                 else:
                     UpperCamelCase_          =		2 * i + 1
             UpperCamelCase_          =		self.__heap[i]
             if self.__heap[i] < self.__heap[bigger_child]:
                 UpperCamelCase_          =		self.__heap[bigger_child]
                 UpperCamelCase_          =		temporary
             UpperCamelCase_          =		bigger_child






     def      UpperCAmelCase_							(							self     )->			int:
         UpperCamelCase_          =		self.__heap[1]
         UpperCamelCase_          =		self.__heap[self.__size]
         self.__size -= 1
         self.__heap.pop()
         self.__swap_down(1     )
         return max_value






     @property
     def      UpperCAmelCase_							(							self     )->			List[Any]:
         return self.__heap[1:]






     def __len__(							self     )->			List[str]:
         return self.__size


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # create an instance of BinaryHeap
 SCREAMING_SNAKE_CASE						:Optional[int]      =			BinaryHeap()
 binary_heap.insert(6)
 binary_heap.insert(10)
 binary_heap.insert(15)
 binary_heap.insert(12)
 # pop root(max-values because it is max heap)
 print(binary_heap.pop())  # 15
 print(binary_heap.pop())  # 12
 # get the list and size after operations
 print(binary_heap.get_list)
 print(len(binary_heap))
