



'''simple docstring'''

import torch
from torch import nn






class 						a_		(     nn.Module    ):


							def __init__(				self   :						int      ,						a_   :						Optional[int]      ,						a_   :						Dict      ,						a_   :						Dict      ,						a_   :						List[Any]      ,						a_   :						Union[str, Any]=1      ,						a_   :						int=False			)			->					List[str]:
										super().__init__()

										snake_case:    Optional[int]      							=n_token
										snake_case:    int      							=d_embed
										snake_case:    Any      							=d_proj

										snake_case:    str      							=cutoffs + [n_token]
										snake_case:    Union[str, Any]      							=[0] + self.cutoffs
										snake_case:    Tuple      							=div_val

										snake_case:    str      							=self.cutoffs[0]
										snake_case:    Optional[Any]      							=len(self.cutoffs			) - 1
										snake_case:    Dict      							=self.shortlist_size + self.n_clusters

										if self.n_clusters > 0:
													snake_case:    Tuple      							=nn.Parameter(torch.zeros(self.n_clusters      ,						self.d_embed			)			)
													snake_case:    str      							=nn.Parameter(torch.zeros(self.n_clusters			)			)

										snake_case:    List[Any]      							=nn.ModuleList()
										snake_case:    Any      							=nn.ParameterList()

										if div_val == 1:
													for i in range(len(self.cutoffs			)			):
																if d_proj != d_embed:
																			self.out_projs.append(nn.Parameter(torch.FloatTensor(a_      ,						a_			)			)			)
																else:
																			self.out_projs.append(a_			)

													self.out_layers.append(nn.Linear(a_      ,						a_			)			)
										else:
													for i in range(len(self.cutoffs			)			):
																snake_case							,  snake_case:    Union[str, Any]      							=self.cutoff_ends[i], self.cutoff_ends[i + 1]
																snake_case:    Optional[int]      							=d_embed // (div_val**i)

																self.out_projs.append(nn.Parameter(torch.FloatTensor(a_      ,						a_			)			)			)

																self.out_layers.append(nn.Linear(a_      ,						r_idx - l_idx			)			)

										snake_case:    Dict      							=keep_order


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						a_   :						List[Any]      ,						a_   :						Optional[Any]      ,						a_   :						List[Any]      ,						a_   :						Dict			)			->					str:
										if proj is None:
													snake_case:    Optional[int]      							=nn.functional.linear(a_      ,						a_      ,						bias=a_			)
										else:
													# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
													snake_case:    str      							=nn.functional.linear(a_      ,						proj.t().contiguous()			)
													snake_case:    List[str]      							=nn.functional.linear(a_      ,						a_      ,						bias=a_			)
													# else:
													#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
													#     if bias is not None:
													#         logit = logit + bias

										return logit


							def    UpperCamelCase     (				self   :						int      ,						a_   :						Any      ,						a_   :						List[str]=None      ,						a_   :						Optional[Any]=False			)			->					Dict:

										if labels is not None:
													# Shift so that tokens < n predict n
													snake_case:    Optional[int]      							=hidden[..., :-1, :].contiguous()
													snake_case:    List[Any]      							=labels[..., 1:].contiguous()
													snake_case:    Optional[int]      							=hidden.view(-1      ,						hidden.size(-1			)			)
													snake_case:    List[str]      							=labels.view(-1			)
													if hidden.size(0			) != labels.size(0			):
																raise RuntimeError('Input and labels should have the same size in the batch dimension.'			)
										else:
													snake_case:    str      							=hidden.view(-1      ,						hidden.size(-1			)			)

										if self.n_clusters == 0:
													snake_case:    List[Any]      							=self._compute_logit(a_      ,						self.out_layers[0].weight      ,						self.out_layers[0].bias      ,						self.out_projs[0]			)
													if labels is not None:
																snake_case:    str      							=labels != -1_0_0
																snake_case:    Dict      							=torch.zeros_like(a_      ,						dtype=hidden.dtype      ,						device=hidden.device			)
																snake_case:    Any      							=(
																    -nn.functional.log_softmax(a_      ,						dim=-1			)[mask].gather(1      ,						labels[mask].unsqueeze(1			)			).squeeze(1			)
																)
													else:
																snake_case:    List[str]      							=nn.functional.log_softmax(a_      ,						dim=-1			)
										else:
													# construct weights and biases
													snake_case							,  snake_case:    Dict      							=[], []
													for i in range(len(self.cutoffs			)			):
																if self.div_val == 1:
																			snake_case							,  snake_case:    Dict      							=self.cutoff_ends[i], self.cutoff_ends[i + 1]
																			snake_case:    List[str]      							=self.out_layers[0].weight[l_idx:r_idx]
																			snake_case:    Any      							=self.out_layers[0].bias[l_idx:r_idx]
																else:
																			snake_case:    Any      							=self.out_layers[i].weight
																			snake_case:    str      							=self.out_layers[i].bias

																if i == 0:
																			snake_case:    Optional[Any]      							=torch.cat([weight_i, self.cluster_weight]      ,						dim=0			)
																			snake_case:    Union[str, Any]      							=torch.cat([bias_i, self.cluster_bias]      ,						dim=0			)

																weights.append(a_			)
																biases.append(a_			)

													snake_case							,  snake_case							,  snake_case:    Dict      							=weights[0], biases[0], self.out_projs[0]

													snake_case:    Dict      							=self._compute_logit(a_      ,						a_      ,						a_      ,						a_			)
													snake_case:    Optional[int]      							=nn.functional.log_softmax(a_      ,						dim=1			)

													if labels is None:
																snake_case:    str      							=hidden.new_empty((head_logit.size(0			), self.n_token)			)
													else:
																snake_case:    Union[str, Any]      							=torch.zeros_like(a_      ,						dtype=hidden.dtype      ,						device=hidden.device			)

													snake_case:    Optional[Any]      							=0
													snake_case:    Union[str, Any]      							=[0] + self.cutoffs
													for i in range(len(a_			) - 1			):
																snake_case							,  snake_case:    str      							=cutoff_values[i], cutoff_values[i + 1]

																if labels is not None:
																			snake_case:    Optional[int]      							=(labels >= l_idx) & (labels < r_idx)
																			snake_case:    List[Any]      							=mask_i.nonzero().squeeze()

																			if indices_i.numel() == 0:
																						continue

																			snake_case:    Optional[Any]      							=labels.index_select(0      ,						a_			) - l_idx
																			snake_case:    Optional[Any]      							=head_logprob.index_select(0      ,						a_			)
																			snake_case:    List[str]      							=hidden.index_select(0      ,						a_			)
																else:
																			snake_case:    Optional[Any]      							=hidden

																if i == 0:
																			if labels is not None:
																						snake_case:    int      							=head_logprob_i.gather(1      ,						target_i[:, None]			).squeeze(1			)
																			else:
																						snake_case:    List[Any]      							=head_logprob[:, : self.cutoffs[0]]
																else:
																			snake_case							,  snake_case							,  snake_case:    Optional[Any]      							=weights[i], biases[i], self.out_projs[i]

																			snake_case:    Tuple      							=self._compute_logit(a_      ,						a_      ,						a_      ,						a_			)
																			snake_case:    List[Any]      							=nn.functional.log_softmax(a_      ,						dim=1			)
																			snake_case:    Any      							=self.cutoffs[0] + i - 1  # No probability for the head cluster
																			if labels is not None:
																						snake_case:    List[str]      							=head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																						    1      ,						target_i[:, None]			).squeeze(1			)
																			else:
																						snake_case:    Optional[int]      							=head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																						snake_case:    Optional[int]      							=logprob_i

																if labels is not None:
																			if (hasattr(self      ,						'keep_order'			) and self.keep_order) or keep_order:
																						out.index_copy_(0      ,						a_      ,						-logprob_i			)
																			else:
																						out[offset : offset + logprob_i.size(0			)].copy_(-logprob_i			)
																			offset += logprob_i.size(0			)

										return out




							def    UpperCamelCase     (				self   :						Optional[int]      ,						a_   :						Dict			)			->					Optional[Any]:
										if self.n_clusters == 0:
													snake_case:    Tuple      							=self._compute_logit(a_      ,						self.out_layers[0].weight      ,						self.out_layers[0].bias      ,						self.out_projs[0]			)
													return nn.functional.log_softmax(a_      ,						dim=-1			)
										else:
													# construct weights and biases
													snake_case							,  snake_case:    Optional[int]      							=[], []
													for i in range(len(self.cutoffs			)			):
																if self.div_val == 1:
																			snake_case							,  snake_case:    int      							=self.cutoff_ends[i], self.cutoff_ends[i + 1]
																			snake_case:    Optional[Any]      							=self.out_layers[0].weight[l_idx:r_idx]
																			snake_case:    str      							=self.out_layers[0].bias[l_idx:r_idx]
																else:
																			snake_case:    Any      							=self.out_layers[i].weight
																			snake_case:    str      							=self.out_layers[i].bias

																if i == 0:
																			snake_case:    List[Any]      							=torch.cat([weight_i, self.cluster_weight]      ,						dim=0			)
																			snake_case:    Tuple      							=torch.cat([bias_i, self.cluster_bias]      ,						dim=0			)

																weights.append(a_			)
																biases.append(a_			)

													snake_case							,  snake_case							,  snake_case:    Tuple      							=weights[0], biases[0], self.out_projs[0]
													snake_case:    Any      							=self._compute_logit(a_      ,						a_      ,						a_      ,						a_			)

													snake_case:    Dict      							=hidden.new_empty((head_logit.size(0			), self.n_token)			)
													snake_case:    Optional[int]      							=nn.functional.log_softmax(a_      ,						dim=1			)

													snake_case:    Tuple      							=[0] + self.cutoffs
													for i in range(len(a_			) - 1			):
																snake_case							,  snake_case:    Dict      							=cutoff_values[i], cutoff_values[i + 1]

																if i == 0:
																			snake_case:    Tuple      							=head_logprob[:, : self.cutoffs[0]]
																else:
																			snake_case							,  snake_case							,  snake_case:    int      							=weights[i], biases[i], self.out_projs[i]

																			snake_case:    Optional[int]      							=self._compute_logit(a_      ,						a_      ,						a_      ,						a_			)
																			snake_case:    Union[str, Any]      							=nn.functional.log_softmax(a_      ,						dim=1			)

																			snake_case:    Optional[Any]      							=head_logprob[:, -i] + tail_logprob_i
																			snake_case:    Tuple      							=logprob_i

													return out









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







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





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




