'''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    :			Dict,      _lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int],      _lowerCAmelCase    :			int,      self    :			str)    ->		List[Any]:



						"""simple docstring"""

						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)    ->		nn.Linear:



						"""simple docstring"""

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




class 		SCREAMING_SNAKE_CASE_					(nn.Module      ):



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_					):
									super().__init__()
									snake_case :       str				=    nf
									snake_case :       Optional[int]				=    nn.Parameter(torch.empty(snake_case_				,			snake_case_					)					)
									snake_case :       Union[str, Any]				=    nn.Parameter(torch.zeros(snake_case_					)					)
									nn.init.normal_(self.weight				,			std=0.02					)






			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       Dict				=    x.size()[:-1] + (self.nf,)
									snake_case :       Optional[int]				=    torch.addmm(self.bias				,			x.view(-1				,			x.size(-1					)					)				,			self.weight					)
									snake_case :       Union[str, Any]				=    x.view(snake_case_					)
									return x







def        UpperCAmelCase__   (				_lowerCAmelCase    :			ConvaD,      _lowerCAmelCase    :			torch.LongTensor,      _lowerCAmelCase    :			int = 1)    ->		ConvaD:



						"""simple docstring"""

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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[nn.Linear, ConvaD],      _lowerCAmelCase    :			torch.LongTensor,      _lowerCAmelCase    :			Optional[int] = None)    ->		Union[nn.Linear, ConvaD]:



						"""simple docstring"""

						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    :			str)    ->		torch.Tensor:



						"""simple docstring"""

						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
						snake_case :       Tuple				=    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:
												snake_case :       List[Any]				=    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 :       Tuple				=    input_tensors[0].shape[chunk_dim] // chunk_size

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



						"""simple docstring"""

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


def        UpperCAmelCase__   (				*_lowerCAmelCase    :			Union[torch.Tensor, List[torch.Tensor]],      _lowerCAmelCase    :			Optional[str] = None)    ->		Tuple[torch.Tensor, ...]:



						"""simple docstring"""

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



def        UpperCAmelCase__   (				_lowerCAmelCase    :			torch.Tensor)    ->		Tuple[torch.device, int, int]:



						"""simple docstring"""

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






'''simple docstring'''

def        UpperCAmelCase__   (				_lowerCAmelCase    :			int = 400_0000)    ->		int:



						"""simple docstring"""

						snake_case :       List[Any]				=    1
						snake_case :       int				=    2
						snake_case :       List[str]				=    0
						while j <= n:
												if j % 2 == 0:
																		total += j
												snake_case				,					snake_case :       Any				=    j, i + j

						return total


if __name__ == "__main__":
						print(f"{solution() = }")





