"""simple docstring"""






import torch
from torch import nn

class lowerCamelCase		(nn.Module   ):






					'''simple docstring'''


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

									SCREAMING_SNAKE_CASE__			=					n_token
									SCREAMING_SNAKE_CASE__			=					d_embed
									SCREAMING_SNAKE_CASE__			=					d_proj

									SCREAMING_SNAKE_CASE__			=					cutoffs + [n_token]
									SCREAMING_SNAKE_CASE__			=					[0] + self.cutoffs
									SCREAMING_SNAKE_CASE__			=					div_val

									SCREAMING_SNAKE_CASE__			=					self.cutoffs[0]
									SCREAMING_SNAKE_CASE__			=					len(self.cutoffs		) - 1
									SCREAMING_SNAKE_CASE__			=					self.shortlist_size + self.n_clusters

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

									SCREAMING_SNAKE_CASE__			=					nn.ModuleList()
									SCREAMING_SNAKE_CASE__			=					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(_snake_case ,			_snake_case		)		)		)
																	else:
																					self.out_projs.append(_snake_case		)

													self.out_layers.append(nn.Linear(_snake_case ,			_snake_case		)		)
									else:
													for i in range(len(self.cutoffs		)		):
																	SCREAMING_SNAKE_CASE__			=					self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	SCREAMING_SNAKE_CASE__			=					d_embed // (div_val**i)

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

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

									SCREAMING_SNAKE_CASE__			=					keep_order



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

									return logit



					def       lowerCAmelCase_       (    self      :					str ,			_snake_case      :					Dict ,			_snake_case      :					int=None ,			_snake_case      :					List[str]=False		)				->							str:

									if labels is not None:
													# Shift so that tokens < n predict n
													SCREAMING_SNAKE_CASE__			=					hidden[..., :-1, :].contiguous()
													SCREAMING_SNAKE_CASE__			=					labels[..., 1:].contiguous()
													SCREAMING_SNAKE_CASE__			=					hidden.view(-1 ,			hidden.size(-1		)		)
													SCREAMING_SNAKE_CASE__			=					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:
													SCREAMING_SNAKE_CASE__			=					hidden.view(-1 ,			hidden.size(-1		)		)

									if self.n_clusters == 0:
													SCREAMING_SNAKE_CASE__			=					self._compute_logit(_snake_case ,			self.out_layers[0].weight ,			self.out_layers[0].bias ,			self.out_projs[0]		)
													if labels is not None:
																	SCREAMING_SNAKE_CASE__			=					labels != -100
																	SCREAMING_SNAKE_CASE__			=					torch.zeros_like(_snake_case ,			dtype=hidden.dtype ,			device=hidden.device		)
																	SCREAMING_SNAKE_CASE__			=					(
																	    -nn.functional.log_softmax(_snake_case ,			dim=-1		)[mask].gather(1 ,			labels[mask].unsqueeze(1		)		).squeeze(1		)
																	)
													else:
																	SCREAMING_SNAKE_CASE__			=					nn.functional.log_softmax(_snake_case ,			dim=-1		)
									else:
													# construct weights and biases
													SCREAMING_SNAKE_CASE__			=					[], []
													for i in range(len(self.cutoffs		)		):
																	if self.div_val == 1:
																					SCREAMING_SNAKE_CASE__			=					self.cutoff_ends[i], self.cutoff_ends[i + 1]
																					SCREAMING_SNAKE_CASE__			=					self.out_layers[0].weight[l_idx:r_idx]
																					SCREAMING_SNAKE_CASE__			=					self.out_layers[0].bias[l_idx:r_idx]
																	else:
																					SCREAMING_SNAKE_CASE__			=					self.out_layers[i].weight
																					SCREAMING_SNAKE_CASE__			=					self.out_layers[i].bias

																	if i == 0:
																					SCREAMING_SNAKE_CASE__			=					torch.cat([weight_i, self.cluster_weight] ,			dim=0		)
																					SCREAMING_SNAKE_CASE__			=					torch.cat([bias_i, self.cluster_bias] ,			dim=0		)

																	weights.append(_snake_case		)
																	biases.append(_snake_case		)

													SCREAMING_SNAKE_CASE__			=					weights[0], biases[0], self.out_projs[0]

													SCREAMING_SNAKE_CASE__			=					self._compute_logit(_snake_case ,			_snake_case ,			_snake_case ,			_snake_case		)
													SCREAMING_SNAKE_CASE__			=					nn.functional.log_softmax(_snake_case ,			dim=1		)

													if labels is None:
																	SCREAMING_SNAKE_CASE__			=					hidden.new_empty((head_logit.size(0		), self.n_token)		)
													else:
																	SCREAMING_SNAKE_CASE__			=					torch.zeros_like(_snake_case ,			dtype=hidden.dtype ,			device=hidden.device		)

													SCREAMING_SNAKE_CASE__			=					0
													SCREAMING_SNAKE_CASE__			=					[0] + self.cutoffs
													for i in range(len(_snake_case		) - 1		):
																	SCREAMING_SNAKE_CASE__			=					cutoff_values[i], cutoff_values[i + 1]

																	if labels is not None:
																					SCREAMING_SNAKE_CASE__			=					(labels >= l_idx) & (labels < r_idx)
																					SCREAMING_SNAKE_CASE__			=					mask_i.nonzero().squeeze()

																					if indices_i.numel() == 0:
																									continue

																					SCREAMING_SNAKE_CASE__			=					labels.index_select(0 ,			_snake_case		) - l_idx
																					SCREAMING_SNAKE_CASE__			=					head_logprob.index_select(0 ,			_snake_case		)
																					SCREAMING_SNAKE_CASE__			=					hidden.index_select(0 ,			_snake_case		)
																	else:
																					SCREAMING_SNAKE_CASE__			=					hidden

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

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

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

									return out



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

																	if i == 0:
																					SCREAMING_SNAKE_CASE__			=					torch.cat([weight_i, self.cluster_weight] ,			dim=0		)
																					SCREAMING_SNAKE_CASE__			=					torch.cat([bias_i, self.cluster_bias] ,			dim=0		)

																	weights.append(_snake_case		)
																	biases.append(_snake_case		)

													SCREAMING_SNAKE_CASE__			=					weights[0], biases[0], self.out_projs[0]
													SCREAMING_SNAKE_CASE__			=					self._compute_logit(_snake_case ,			_snake_case ,			_snake_case ,			_snake_case		)

													SCREAMING_SNAKE_CASE__			=					hidden.new_empty((head_logit.size(0		), self.n_token)		)
													SCREAMING_SNAKE_CASE__			=					nn.functional.log_softmax(_snake_case ,			dim=1		)

													SCREAMING_SNAKE_CASE__			=					[0] + self.cutoffs
													for i in range(len(_snake_case		) - 1		):
																	SCREAMING_SNAKE_CASE__			=					cutoff_values[i], cutoff_values[i + 1]

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

																					SCREAMING_SNAKE_CASE__			=					self._compute_logit(_snake_case ,			_snake_case ,			_snake_case ,			_snake_case		)
																					SCREAMING_SNAKE_CASE__			=					nn.functional.log_softmax(_snake_case ,			dim=1		)

																					SCREAMING_SNAKE_CASE__			=					head_logprob[:, -i] + tail_logprob_i
																					SCREAMING_SNAKE_CASE__			=					logprob_i

													return out






import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class   __lowercase						:
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   None
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   True
	_A       =   None
	_A       =   1
	_A       =   None
	_A       =   False
	_A       =   None
	_A       =   None






	def       _a(self				: str    ) ->			"DownloadConfig":
							return self.__class__(**{k: copy.deepcopy(snake_case    ) for k, v in self.__dict__.items()}    )




