





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


__lowercase							:int							=	[nn.LayerNorm]

__lowercase							:str							=	logging.get_logger(__name__)

__lowercase							:int							=	version.parse(version.parse(torch.__version__).base_version)

__lowercase							:int							=	parsed_torch_version_base >= version.parse("2.0")
__lowercase							:str							=	parsed_torch_version_base >= version.parse("1.12")
__lowercase							:int							=	parsed_torch_version_base >= version.parse("1.11")
__lowercase							:List[str]							=	parsed_torch_version_base < version.parse("1.11")







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





						'''simple docstring'''


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



class  _a  (					nn.Module				):






							"""simple docstring"""

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






							def   A_	(   self					:  int				,						a					:  Optional[Any]     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       x.size()[:-1] + (self.nf,)
													SCREAMING_SNAKE_CASE__  :					List[Any] =       torch.addmm(self.bias				,						x.view(-1				,						x.size(-1     )     )				,						self.weight     )
													SCREAMING_SNAKE_CASE__  :					Dict =       x.view(a     )
													return x







def    UpperCAmelCase				(  _lowerCamelCase   :	ConvaD      , _lowerCamelCase   :	torch.LongTensor      , _lowerCamelCase   :	int = 1  ):





						'''simple docstring'''


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







def    UpperCAmelCase				(  _lowerCamelCase   :	Union[nn.Linear, ConvaD]      , _lowerCamelCase   :	torch.LongTensor      , _lowerCamelCase   :	Optional[int] = None  ):





						'''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   :	Union[str, Any]  ):





						'''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
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       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:
												SCREAMING_SNAKE_CASE__  :					int =       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}"""  )

												SCREAMING_SNAKE_CASE__  :					Optional[int] =       input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												SCREAMING_SNAKE_CASE__  :					Any =       tuple(input_tensor.chunk(_lowerCamelCase      , dim=_lowerCamelCase  ) for input_tensor in input_tensors  )
												# apply forward fn to every tuple
												SCREAMING_SNAKE_CASE__  :					List[str] =       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]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       torch.ones(_lowerCamelCase      , _lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       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
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       head - sum(1 if h < head else 0 for h in already_pruned_heads  )
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       0
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       mask.view(-1  ).contiguous().eq(1  )
						SCREAMING_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  ):





						'''simple docstring'''


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







def    UpperCAmelCase				(  _lowerCamelCase   :	torch.Tensor  ):





						'''simple docstring'''


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






from itertools import combinations_with_replacement







def    UpperCAmelCase				(  _lowerCamelCase   :	int = 100  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					list[int] =       [*list(range(1      , 21  )  ), 25]
						SCREAMING_SNAKE_CASE__  :					list[int] =       [2 * x for x in range(1      , 21  )] + [50]
						SCREAMING_SNAKE_CASE__  :					list[int] =       [3 * x for x in range(1      , 21  )]
						SCREAMING_SNAKE_CASE__  :					list[int] =       singles + doubles + triples + [0]

						SCREAMING_SNAKE_CASE__  :					int =       0
						SCREAMING_SNAKE_CASE__  :					int
						SCREAMING_SNAKE_CASE__  :					int
						SCREAMING_SNAKE_CASE__  :					int
						SCREAMING_SNAKE_CASE__  :					int

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(_lowerCamelCase      , 2  ):
																		SCREAMING_SNAKE_CASE__  :					int =       double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


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