


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


snake_case_  :     Dict							  =							[nn.LayerNorm]

snake_case_  :     List[Any]							  =							logging.get_logger(__name__)

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

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







def   lowerCamelCase(				a__		,a__		,a__		,a__		,self):
				from torch import _softmax_backward_data

				if is_torch_less_than_1_11:
								return _softmax_backward_data(a__		,a__		,parent.dim		,self)
				else:
								return _softmax_backward_data(a__		,a__		,parent.dim		,self.dtype)







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

class   A__							(						nn.Module					):




						def __init__(    self   : Tuple			,      _a   : Optional[int]			,      _a   : Dict					)							->       List[str]:


										"""simple docstring"""





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




						def 							__UpperCamelCase			(    self   : Any			,      _a   : Any					)							->       str:


										"""simple docstring"""





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







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







def   lowerCamelCase(				a__		,a__		,a__ = None):
				if isinstance(a__		,nn.Linear):
								return prune_linear_layer(a__		,a__		,dim=0 if dim is None else dim)
				elif isinstance(a__		,a__):
								return prune_convad_layer(a__		,a__		,dim=1 if dim is None else dim)
				else:
								raise ValueError(f"Can't prune layer of class {layer.__class__}")







def   lowerCamelCase(				a__		,a__		,a__		,*a__):
				assert len(a__) > 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     					=len(inspect.signature(a__).parameters)
				if num_args_in_forward_chunk_fn != len(a__):
								raise ValueError(
								    f"forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(a__)} input "
								    '''tensors are given''')

				if chunk_size > 0:
								_SCREAMING_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}")

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

								# chunk input tensor into tuples
								_SCREAMING_SNAKE_CASE     					=tuple(input_tensor.chunk(a__		,dim=a__) for input_tensor in input_tensors)
								# apply forward fn to every tuple
								_SCREAMING_SNAKE_CASE     					=tuple(forward_fn(*a__) for input_tensors_chunk in zip(*a__))
								# concatenate output at same dimension
								return torch.cat(a__		,dim=a__)

				return forward_fn(*a__)







def   lowerCamelCase(				a__		,a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE     					=torch.ones(a__		,a__)
				_SCREAMING_SNAKE_CASE     					=set(a__) - 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     					=head - sum(1 if h < head else 0 for h in already_pruned_heads)
								_SCREAMING_SNAKE_CASE     					=0
				_SCREAMING_SNAKE_CASE     					=mask.view(-1).contiguous().eq(1)
				_SCREAMING_SNAKE_CASE     					=torch.arange(len(a__))[mask].long()
				return heads, index







def   lowerCamelCase(				*a__		,a__ = None):
				return torch.meshgrid(*a__		,indexing=a__)







def   lowerCamelCase(				a__):
				return tensor.device, storage_ptr(a__), storage_size(a__)



import math

class   A__							:




						def __init__(    self   : Optional[int]			,      _a   : int			,      _a   : List[Any]			,      _a   : int					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=x
										_SCREAMING_SNAKE_CASE     					=y
										_SCREAMING_SNAKE_CASE     					=z




						def __repr__(    self   : List[str]					)							->       str:


										"""simple docstring"""





										return f"Point({self.x}, {self.y}, {self.z})"







def   lowerCamelCase(				a__		,a__):
				return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))


if __name__ == "__main__":
							import doctest

							doctest.testmod()