





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       :					Optional[int]     					=     [nn.LayerNorm]

__A       :					Union[str, Any]     					=     logging.get_logger(__name__)

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

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





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    self				)						->       Tuple:







			'''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 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__ = 0				)						->       nn.Linear:







			'''simple docstring'''

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

class 			A_ (nn.Module    ):
	def __init__(					self							,      _A							,      _A	):






				'''simple docstring'''







				super().__init__()
				UpperCAmelCase     	=       nf
				UpperCAmelCase     	=       nn.Parameter(torch.empty(_A							,      _A	)	)
				UpperCAmelCase     	=       nn.Parameter(torch.zeros(_A	)	)
				nn.init.normal_(self.weight							,      std=0.02	)







	def   _lowercase       (					self							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       x.size()[:-1] + (self.nf,)
				UpperCAmelCase     	=       torch.addmm(self.bias							,      x.view(-1							,      x.size(-1	)	)							,      self.weight	)
				UpperCAmelCase     	=       x.view(_A	)
				return x





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__ = 1				)						->       ConvaD:







			'''simple docstring'''

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





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__ = None				)						->       Union[nn.Linear, ConvaD]:







			'''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 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    *UpperCamelCase__				)						->       torch.Tensor:







			'''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
			UpperCAmelCase     	=       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:
						UpperCAmelCase     	=       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}"""				)

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

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







			'''simple docstring'''

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





def 		__SCREAMING_SNAKE_CASE						(       *UpperCamelCase__    ,    UpperCamelCase__ = None				)						->       Tuple[torch.Tensor, ...]:







			'''simple docstring'''

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






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







			'''simple docstring'''

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










from __future__ import annotations





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__				)						->       bool:







			'''simple docstring'''

			UpperCAmelCase     	=       42
			UpperCAmelCase     	=       list(UpperCamelCase__				)
			UpperCAmelCase	,    UpperCAmelCase     	=       _vector[:-1], int(_vector[-1]				)
			UpperCAmelCase     	=       [int(UpperCamelCase__				) for digit in __vector]

			vector.reverse()
			for i, digit in enumerate(UpperCamelCase__				):
						if i & 1 == 0:
									UpperCAmelCase     	=       digit * 2
									if doubled > 9:
												doubled -= 9
									check_digit += doubled
						else:
									check_digit += digit

			return check_digit % 10 == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				assert is_luhn("79927398713")
				assert not is_luhn("79927398714")



