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

class     a (					nn.Module     ):




				def __init__(				self						:					str,     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[Any]   ):
										super().__init__()
										snake_case							:  Union[str, Any]			=     nf
										snake_case							:  Union[str, Any]			=     nn.Parameter(torch.empty(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  List[Any]			=     nn.Parameter(torch.zeros(SCREAMING_SNAKE_CASE_   )   )
										nn.init.normal_(self.weight,     std=0.02   )




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										snake_case							:  Dict			=     x.size()[:-1] + (self.nf,)
										snake_case							:  Any			=     torch.addmm(self.bias,     x.view(-1,     x.size(-1   )   ),     self.weight   )
										snake_case							:  int			=     x.view(SCREAMING_SNAKE_CASE_   )
										return x




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

def   A			(							A_				:       Union[nn.Linear, ConvaD]     ,				A_				:       torch.LongTensor     ,				A_				:       Optional[int] = 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   A			(							A_				:       Callable[..., torch.Tensor]     ,				A_				:       int     ,				A_				:       int     ,				*A_				:       Optional[int]							):
						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
						snake_case							:  Optional[Any]			=     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:
												snake_case							:  Union[str, 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}"""							)

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

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

def   A			(							*A_				:       Union[torch.Tensor, List[torch.Tensor]]     ,				A_				:       Optional[str] = None							):
						return torch.meshgrid(*A_     ,				indexing=A_							)





def   A			(							A_				:       torch.Tensor							):
						return tensor.device, storage_ptr(A_							), storage_size(A_							)



from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowerCamelCase__        =						load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__        =						train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

lowerCamelCase__        =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase__        =						[[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase__        =						knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)