



"""simple docstring"""




import torch
from torch import nn
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  Optional[Any],   __lowercase   :  Tuple,   __lowercase   :  str,   __lowercase   :  Union[str, Any],   __lowercase   :  List[Any],   __lowercase   :  Dict=1,   __lowercase   :  Dict=False  ):
							super().__init__()

							lowercase__   					=			n_token
							lowercase__   					=			d_embed
							lowercase__   					=			d_proj

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

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

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

							lowercase__   					=			nn.ModuleList()
							lowercase__   					=			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(__UpperCAmelCase,   __UpperCAmelCase  )  )  )
													else:
																self.out_projs.append(__UpperCAmelCase  )

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

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

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

							lowercase__   					=			keep_order




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

							return logit




				def 							A__ (  self   :  Dict,   __lowercase   :  int,   __lowercase   :  List[str]=None,   __lowercase   :  List[Any]=False  ):

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

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

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

													weights.append(__UpperCAmelCase  )
													biases.append(__UpperCAmelCase  )

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

										lowercase__   					=			self._compute_logit(__UpperCAmelCase,   __UpperCAmelCase,   __UpperCAmelCase,   __UpperCAmelCase  )
										lowercase__   					=			nn.functional.log_softmax(__UpperCAmelCase,   dim=1  )

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

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

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

																if indices_i.numel() == 0:
																			continue

																lowercase__   					=			labels.index_select(0,   __UpperCAmelCase  ) - l_idx
																lowercase__   					=			head_logprob.index_select(0,   __UpperCAmelCase  )
																lowercase__   					=			hidden.index_select(0,   __UpperCAmelCase  )
													else:
																lowercase__   					=			hidden

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

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

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

							return out




				def 							A__ (  self   :  Any,   __lowercase   :  int  ):
							if self.n_clusters == 0:
										lowercase__   					=			self._compute_logit(__UpperCAmelCase,   self.out_layers[0].weight,   self.out_layers[0].bias,   self.out_projs[0]  )
										return nn.functional.log_softmax(__UpperCAmelCase,   dim=-1  )
							else:
										# construct weights and biases
										lowercase__   					=			[], []
										for i in range(len(self.cutoffs  )  ):
													if self.div_val == 1:
																lowercase__   					=			self.cutoff_ends[i], self.cutoff_ends[i + 1]
																lowercase__   					=			self.out_layers[0].weight[l_idx:r_idx]
																lowercase__   					=			self.out_layers[0].bias[l_idx:r_idx]
													else:
																lowercase__   					=			self.out_layers[i].weight
																lowercase__   					=			self.out_layers[i].bias

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

													weights.append(__UpperCAmelCase  )
													biases.append(__UpperCAmelCase  )

										lowercase__   					=			weights[0], biases[0], self.out_projs[0]
										lowercase__   					=			self._compute_logit(__UpperCAmelCase,   __UpperCAmelCase,   __UpperCAmelCase,   __UpperCAmelCase  )

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

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

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

																lowercase__   					=			self._compute_logit(__UpperCAmelCase,   __UpperCAmelCase,   __UpperCAmelCase,   __UpperCAmelCase  )
																lowercase__   					=			nn.functional.log_softmax(__UpperCAmelCase,   dim=1  )

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

										return out









from pathlib import Path

import fire



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
			for path in src_dir.iterdir():
						lowercase__   					=			[x.rstrip() for x in list(path.open().readlines()					)][:n]
						lowercase__   					=			dest_dir.joinpath(path.name					)
						print(SCREAMING_SNAKE_CASE_					)
						dest_path.open("w"					).write("\n".join(SCREAMING_SNAKE_CASE_					)					)


if __name__ == "__main__":
							fire.Fire(minify)




