





"""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_	 =[nn.LayerNorm]

UpperCamelCase_	 =logging.get_logger(__name__)

UpperCamelCase_	 =version.parse(version.parse(torch.__version__).base_version)

UpperCamelCase_	 =parsed_torch_version_base >= version.parse("""2.0""")
UpperCamelCase_	 =parsed_torch_version_base >= version.parse("""1.12""")
UpperCamelCase_	 =parsed_torch_version_base >= version.parse("""1.11""")
UpperCamelCase_	 =parsed_torch_version_base < version.parse("""1.11""")





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




class 					_a		( nn.Module							):






				def __init__(					self						:       str,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[Any]				)      -> int:




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Union[str, Any]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       x.size()[:-1] + (self.nf,)
										_UpperCamelCase     :   Tuple      =       torch.addmm(self.bias,					x.view(-1,					x.size(-1				)				),					self.weight				)
										_UpperCamelCase     :   List[str]      =       x.view(lowerCAmelCase__				)
										return x





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





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase = None  ):
						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  ):
						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
						_UpperCamelCase     :   str      =       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:
												_UpperCamelCase     :   Tuple      =       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     :   Optional[Any]      =       input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												_UpperCamelCase     :   List[str]      =       tuple(input_tensor.chunk(_lowercase			,		dim=_lowercase  ) for input_tensor in input_tensors  )
												# apply forward fn to every tuple
												_UpperCamelCase     :   List[str]      =       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  ):
						_UpperCamelCase     :   Any      =       torch.ones(_lowercase			,		_lowercase  )
						_UpperCamelCase     :   Optional[Any]      =       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
												_UpperCamelCase     :   Tuple      =       head - sum(1 if h < head else 0 for h in already_pruned_heads  )
												_UpperCamelCase     :   Optional[int]      =       0
						_UpperCamelCase     :   List[Any]      =       mask.view(-1  ).contiguous().eq(1  )
						_UpperCamelCase     :   torch.LongTensor      =       torch.arange(len(_lowercase  )  )[mask].long()
						return heads, index





def 			a_							(				*_lowercase			,		_lowercase = None  ):
						return torch.meshgrid(*_lowercase			,		indexing=_lowercase  )






def 			a_							(				_lowercase  ):
						return tensor.device, storage_ptr(_lowercase  ), storage_size(_lowercase  )









import numpy as np




def  a_		(							__snake_case     ,		__snake_case					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= abs(predict - actual					)
			UpperCamelCase_     				= difference.mean()

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)

			UpperCamelCase_     				= square_diff.mean()
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()
			UpperCamelCase_     				= np.sqrt(__snake_case					)
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= np.log(predict + 1					)
			UpperCamelCase_     				= np.log(actual + 1					)

			UpperCamelCase_     				= log_predict - log_actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()

			UpperCamelCase_     				= np.sqrt(__snake_case					)

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			# print(numerator, denumerator)
			UpperCamelCase_     				= float(__snake_case					) / denumerator * 1_0_0

			return score





def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			return np.mean(np.array(__snake_case					) == np.array(__snake_case					)					)
