"""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


UpperCAmelCase       :    Optional[Any]									=			[nn.LayerNorm]

UpperCAmelCase       :    Optional[Any]									=			logging.get_logger(__name__)

UpperCAmelCase       :    Any									=			version.parse(version.parse(torch.__version__).base_version)

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



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase							, self						):







					"""simple docstring"""





					from torch import _softmax_backward_data

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



def 				__a       (			_lowercase							, _lowercase							, _lowercase = 0						):







					"""simple docstring"""





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

class        __SCREAMING_SNAKE_CASE     (   nn.Module							):







				def __init__(    self    :Tuple      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :Dict  )      ->					List[Any]:




									"""simple docstring"""



									super().__init__()
									lowerCamelCase__		:   str  				=					nf
									lowerCamelCase__		:   Optional[Any]  				=					nn.Parameter(torch.empty(__UpperCAmelCase      ,__UpperCAmelCase  )  )
									lowerCamelCase__		:   str  				=					nn.Parameter(torch.zeros(__UpperCAmelCase  )  )
									nn.init.normal_(self.weight      ,std=0.02  )







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :Optional[int]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Optional[Any]  				=					x.size()[:-1] + (self.nf,)
									lowerCamelCase__		:   Dict  				=					torch.addmm(self.bias      ,x.view(-1      ,x.size(-1  )  )      ,self.weight  )
									lowerCamelCase__		:   Dict  				=					x.view(__UpperCAmelCase  )
									return x





def 				__a       (			_lowercase							, _lowercase							, _lowercase = 1						):







					"""simple docstring"""





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



def 				__a       (			_lowercase							, _lowercase							, _lowercase = None						):







					"""simple docstring"""





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



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, *_lowercase						):







					"""simple docstring"""





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

					if chunk_size > 0:
										lowerCamelCase__		:   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}"""						)

										lowerCamelCase__		:   Union[str, Any]  				=					input_tensors[0].shape[chunk_dim] // chunk_size

										# chunk input tensor into tuples
										lowerCamelCase__		:   Union[str, Any]  				=					tuple(input_tensor.chunk(_lowercase							, dim=_lowercase						) for input_tensor in input_tensors						)
										# apply forward fn to every tuple
										lowerCamelCase__		:   int  				=					tuple(forward_fn(*_lowercase						) for input_tensors_chunk in zip(*_lowercase						)						)
										# concatenate output at same dimension
										return torch.cat(_lowercase							, dim=_lowercase						)

					return forward_fn(*_lowercase						)



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





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



def 				__a       (			*_lowercase							, _lowercase = None						):







					"""simple docstring"""





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





def 				__a       (			_lowercase						):







					"""simple docstring"""





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







"""simple docstring"""




from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				__a       (			):







					"""simple docstring"""





					lowerCamelCase__		:   Union[str, Any]  				=					HfArgumentParser(_lowercase						)
					try:
										lowerCamelCase__		:   Optional[int]  				=					parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										lowerCamelCase__		:   Tuple  				=					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
										lowerCamelCase__		:   Tuple  				=					''' '''.join(str(_lowercase						).split(''' '''						)[:-1]						)
										lowerCamelCase__		:   Union[str, Any]  				=					''''''
										lowerCamelCase__		:   int  				=					eval(str(_lowercase						).split(''' '''						)[-1]						)
										lowerCamelCase__		:   Any  				=					[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:]						)
															else:
																				wrong_args.append(_lowercase						)
										if len(_lowercase						) > 0:
															lowerCamelCase__		:   Any  				=					full_error_msg + begin_error_msg + str(_lowercase						)
										raise ValueError(_lowercase						)

					lowerCamelCase__		:   List[Any]  				=					PyTorchBenchmark(args=_lowercase						)
					benchmark.run()


if __name__ == "__main__":
					main()






