




"""simple docstring"""




import torch
from torch import nn






class 					__magic_name__							( nn.Module       ):






				'''simple docstring'''





				def __init__( self	,				_a	,				_a	,				_a	,				_a	,				_a=1	,				_a=False				):





							"""simple docstring"""
							super().__init__()

							lowerCamelCase       =      n_token
							lowerCamelCase       =      d_embed
							lowerCamelCase       =      d_proj

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

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

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

							lowerCamelCase       =      nn.ModuleList()
							lowerCamelCase       =      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				)				):
													lowerCamelCase   ,     lowerCamelCase       =      self.cutoff_ends[i], self.cutoff_ends[i + 1]
													lowerCamelCase       =      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				)				)

							lowerCamelCase       =      keep_order





				def      _lowerCAmelCase       ( self	,				_a	,				_a	,				_a	,				_a				):





							"""simple docstring"""
							if proj is None:
										lowerCamelCase       =      nn.functional.linear(_a	,				_a	,				bias=_a				)
							else:
										# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
										lowerCamelCase       =      nn.functional.linear(_a	,				proj.t().contiguous()				)
										lowerCamelCase       =      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      _lowerCAmelCase       ( self	,				_a	,				_a=None	,				_a=False				):





							"""simple docstring"""

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

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

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

													weights.append(_a				)
													biases.append(_a				)

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

										lowerCamelCase       =      self._compute_logit(_a	,				_a	,				_a	,				_a				)
										lowerCamelCase       =      nn.functional.log_softmax(_a	,				dim=1				)

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

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

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

																if indices_i.numel() == 0:
																			continue

																lowerCamelCase       =      labels.index_select(0	,				_a				) - l_idx
																lowerCamelCase       =      head_logprob.index_select(0	,				_a				)
																lowerCamelCase       =      hidden.index_select(0	,				_a				)
													else:
																lowerCamelCase       =      hidden

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

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





							"""simple docstring"""
							if self.n_clusters == 0:
										lowerCamelCase       =      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
										lowerCamelCase   ,     lowerCamelCase       =      [], []
										for i in range(len(self.cutoffs				)				):
													if self.div_val == 1:
																lowerCamelCase   ,     lowerCamelCase       =      self.cutoff_ends[i], self.cutoff_ends[i + 1]
																lowerCamelCase       =      self.out_layers[0].weight[l_idx:r_idx]
																lowerCamelCase       =      self.out_layers[0].bias[l_idx:r_idx]
													else:
																lowerCamelCase       =      self.out_layers[i].weight
																lowerCamelCase       =      self.out_layers[i].bias

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

													weights.append(_a				)
													biases.append(_a				)

										lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      weights[0], biases[0], self.out_projs[0]
										lowerCamelCase       =      self._compute_logit(_a	,				_a	,				_a	,				_a				)

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

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

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

																lowerCamelCase       =      self._compute_logit(_a	,				_a	,				_a	,				_a				)
																lowerCamelCase       =      nn.functional.log_softmax(_a	,				dim=1				)

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

										return out









"""simple docstring"""




import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCAmelCase							:    List[str]  							=							4
lowerCAmelCase							:    List[str]  							=							3






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				pass




def 					a__				(       snake_case__       )		->      Dict:
			for shard in shards:
						for i in range(snake_case__       ):
									yield {"i": i, "shard": shard}



def 					a__				(       )		->      List[Any]:
			lowerCamelCase       =      int(os.environ["""RANK"""]       )
			lowerCamelCase       =      int(os.environ["""WORLD_SIZE"""]       )

			lowerCamelCase       =      ArgumentParser()
			parser.add_argument("""--streaming""" ,				type=snake_case__       )
			parser.add_argument("""--local_rank""" ,				type=snake_case__       )
			parser.add_argument("""--num_workers""" ,				type=snake_case__ ,				default=0       )
			lowerCamelCase       =      parser.parse_args()
			lowerCamelCase       =      args.streaming
			lowerCamelCase       =      args.num_workers

			lowerCamelCase       =      {"""shards""": [F'shard_{shard_idx}' for shard_idx in range(snake_case__       )]}
			lowerCamelCase       =      IterableDataset.from_generator(snake_case__ ,				gen_kwargs=snake_case__       )
			if not streaming:
						lowerCamelCase       =      Dataset.from_list(list(snake_case__       )       )

			lowerCamelCase       =      split_dataset_by_node(snake_case__ ,				rank=snake_case__ ,				world_size=snake_case__       )
			lowerCamelCase       =      torch.utils.data.DataLoader(snake_case__ ,				num_workers=snake_case__       )

			lowerCamelCase       =      NUM_SHARDS * NUM_ITEMS_PER_SHARD
			lowerCamelCase       =      full_size // world_size
			expected_local_size += int(rank < (full_size % world_size)       )

			lowerCamelCase       =      sum(1 for _ in dataloader       )
			if local_size != expected_local_size:
						raise FailedTestError(F'local_size {local_size} != expected_local_size {expected_local_size}'       )


if __name__ == "__main__":
					main()



