




import torch
from torch import nn







class 				lowerCAmelCase_       (					nn.Module							):






				def __init__(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__=1      ,snake_case__=False ):
							super().__init__()

							SCREAMING_SNAKE_CASE_   : Any					  =      n_token
							SCREAMING_SNAKE_CASE_   : Tuple					  =      d_embed
							SCREAMING_SNAKE_CASE_   : int					  =      d_proj

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      cutoffs + [n_token]
							SCREAMING_SNAKE_CASE_   : Dict					  =      [0] + self.cutoffs
							SCREAMING_SNAKE_CASE_   : int					  =      div_val

							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.cutoffs[0]
							SCREAMING_SNAKE_CASE_   : str					  =      len(self.cutoffs ) - 1
							SCREAMING_SNAKE_CASE_   : Any					  =      self.shortlist_size + self.n_clusters

							if self.n_clusters > 0:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      nn.Parameter(torch.zeros(self.n_clusters      ,self.d_embed ) )
										SCREAMING_SNAKE_CASE_   : int					  =      nn.Parameter(torch.zeros(self.n_clusters ) )

							SCREAMING_SNAKE_CASE_   : int					  =      nn.ModuleList()
							SCREAMING_SNAKE_CASE_   : List[str]					  =      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_  ,    SCREAMING_SNAKE_CASE_   : Tuple					  =      self.cutoff_ends[i], self.cutoff_ends[i + 1]
													SCREAMING_SNAKE_CASE_   : List[str]					  =      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_   : Any					  =      keep_order






				def 		snake_case	(			self      ,snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ ):
							if proj is None:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      nn.functional.linear(snake_case__      ,snake_case__      ,bias=snake_case__ )
							else:
										# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
										SCREAMING_SNAKE_CASE_   : int					  =      nn.functional.linear(snake_case__      ,proj.t().contiguous() )
										SCREAMING_SNAKE_CASE_   : Any					  =      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 		snake_case	(			self      ,snake_case__      ,snake_case__=None      ,snake_case__=False ):

							if labels is not None:
										# Shift so that tokens < n predict n
										SCREAMING_SNAKE_CASE_   : Dict					  =      hidden[..., :-1, :].contiguous()
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      labels[..., 1:].contiguous()
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      hidden.view(-1      ,hidden.size(-1 ) )
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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_   : List[Any]					  =      hidden.view(-1      ,hidden.size(-1 ) )

							if self.n_clusters == 0:
										SCREAMING_SNAKE_CASE_   : str					  =      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_   : Tuple					  =      labels != -100
													SCREAMING_SNAKE_CASE_   : Optional[int]					  =      torch.zeros_like(snake_case__      ,dtype=hidden.dtype      ,device=hidden.device )
													SCREAMING_SNAKE_CASE_   : int					  =      (
													    -nn.functional.log_softmax(snake_case__      ,dim=-1 )[mask].gather(1      ,labels[mask].unsqueeze(1 ) ).squeeze(1 )
													)
										else:
													SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      nn.functional.log_softmax(snake_case__      ,dim=-1 )
							else:
										# construct weights and biases
										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      [], []
										for i in range(len(self.cutoffs ) ):
													if self.div_val == 1:
																SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      self.cutoff_ends[i], self.cutoff_ends[i + 1]
																SCREAMING_SNAKE_CASE_   : Any					  =      self.out_layers[0].weight[l_idx:r_idx]
																SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.out_layers[0].bias[l_idx:r_idx]
													else:
																SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.out_layers[i].weight
																SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      self.out_layers[i].bias

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

													weights.append(snake_case__ )
													biases.append(snake_case__ )

										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Any					  =      weights[0], biases[0], self.out_projs[0]

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

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

										SCREAMING_SNAKE_CASE_   : List[str]					  =      0
										SCREAMING_SNAKE_CASE_   : str					  =      [0] + self.cutoffs
										for i in range(len(snake_case__ ) - 1 ):
													SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      cutoff_values[i], cutoff_values[i + 1]

													if labels is not None:
																SCREAMING_SNAKE_CASE_   : List[str]					  =      (labels >= l_idx) & (labels < r_idx)
																SCREAMING_SNAKE_CASE_   : Tuple					  =      mask_i.nonzero().squeeze()

																if indices_i.numel() == 0:
																			continue

																SCREAMING_SNAKE_CASE_   : Any					  =      labels.index_select(0      ,snake_case__ ) - l_idx
																SCREAMING_SNAKE_CASE_   : List[str]					  =      head_logprob.index_select(0      ,snake_case__ )
																SCREAMING_SNAKE_CASE_   : Any					  =      hidden.index_select(0      ,snake_case__ )
													else:
																SCREAMING_SNAKE_CASE_   : Any					  =      hidden

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

																SCREAMING_SNAKE_CASE_   : int					  =      self._compute_logit(snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ )
																SCREAMING_SNAKE_CASE_   : List[str]					  =      nn.functional.log_softmax(snake_case__      ,dim=1 )
																SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.cutoffs[0] + i - 1  # No probability for the head cluster
																if labels is not None:
																			SCREAMING_SNAKE_CASE_   : List[Any]					  =      head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																			    1      ,target_i[:, None] ).squeeze(1 )
																else:
																			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																			SCREAMING_SNAKE_CASE_   : Dict					  =      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 		snake_case	(			self      ,snake_case__ ):
							if self.n_clusters == 0:
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [], []
										for i in range(len(self.cutoffs ) ):
													if self.div_val == 1:
																SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.cutoff_ends[i], self.cutoff_ends[i + 1]
																SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.out_layers[0].weight[l_idx:r_idx]
																SCREAMING_SNAKE_CASE_   : int					  =      self.out_layers[0].bias[l_idx:r_idx]
													else:
																SCREAMING_SNAKE_CASE_   : Any					  =      self.out_layers[i].weight
																SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.out_layers[i].bias

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

													weights.append(snake_case__ )
													biases.append(snake_case__ )

										SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      weights[0], biases[0], self.out_projs[0]
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self._compute_logit(snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ )

										SCREAMING_SNAKE_CASE_   : Tuple					  =      hidden.new_empty((head_logit.size(0 ), self.n_token) )
										SCREAMING_SNAKE_CASE_   : str					  =      nn.functional.log_softmax(snake_case__      ,dim=1 )

										SCREAMING_SNAKE_CASE_   : Any					  =      [0] + self.cutoffs
										for i in range(len(snake_case__ ) - 1 ):
													SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      cutoff_values[i], cutoff_values[i + 1]

													if i == 0:
																SCREAMING_SNAKE_CASE_   : List[Any]					  =      head_logprob[:, : self.cutoffs[0]]
													else:
																SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : List[str]					  =      weights[i], biases[i], self.out_projs[i]

																SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self._compute_logit(snake_case__      ,snake_case__      ,snake_case__      ,snake_case__ )
																SCREAMING_SNAKE_CASE_   : int					  =      nn.functional.log_softmax(snake_case__      ,dim=1 )

																SCREAMING_SNAKE_CASE_   : Any					  =      head_logprob[:, -i] + tail_logprob_i
																SCREAMING_SNAKE_CASE_   : Dict					  =      logprob_i

										return out









import os

import numpy
import onnx





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Optional[Any]  )       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      a.name
			SCREAMING_SNAKE_CASE_   : Dict					  =      b.name

			SCREAMING_SNAKE_CASE_   : str					  =      ''
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      ''

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      a == b

			SCREAMING_SNAKE_CASE_   : str					  =      name_a
			SCREAMING_SNAKE_CASE_   : str					  =      name_b

			return res





def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Dict  )       ->  Optional[int]:







			"""simple docstring"""





			for i, input_name in enumerate(node_proto.input  ):
						if input_name == name:
									node_proto.input.insert(lowerCamelCase_  ,							lowerCamelCase_  )
									node_proto.input.pop(i + 1  )

			if node_proto.op_type == "If":
						_graph_replace_input_with(node_proto.attribute[0].g  ,							lowerCamelCase_  ,							lowerCamelCase_  )
						_graph_replace_input_with(node_proto.attribute[1].g  ,							lowerCamelCase_  ,							lowerCamelCase_  )
			if node_proto.op_type == "Loop":
						_graph_replace_input_with(node_proto.attribute[0].g  ,							lowerCamelCase_  ,							lowerCamelCase_  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Dict  )       ->  List[Any]:







			"""simple docstring"""





			for n in graph_proto.node:
						_node_replace_input_with(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Any  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      list(model.graph.initializer  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      list(model_without_ext.graph.initializer  )
			for i, ref_i in ind_to_replace:
						assert inits_with_data[i].name == inits[i].name
						assert inits_with_data[ref_i].name == inits[ref_i].name
						assert i > ref_i

						SCREAMING_SNAKE_CASE_   : List[str]					  =      inits[i].name
						SCREAMING_SNAKE_CASE_   : str					  =      inits[ref_i].name

						model_without_ext.graph.initializer.remove(inits[i]  )

						# for n in model.graph.node:
						_graph_replace_input_with(model_without_ext.graph  ,							lowerCamelCase_  ,							lowerCamelCase_  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      os.path.dirname(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      os.path.basename(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : int					  =      onnx.load(os.path.join(lowerCamelCase_  ,							lowerCamelCase_  )  )

			SCREAMING_SNAKE_CASE_   : Any					  =      list(model.graph.initializer  )

			SCREAMING_SNAKE_CASE_   : int					  =      set()
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {}
			SCREAMING_SNAKE_CASE_   : Dict					  =      []

			SCREAMING_SNAKE_CASE_   : int					  =      0

			for i in range(len(lowerCamelCase_  )  ):
						if i in dup_set:
									continue

						for j in range(i + 1  ,							len(lowerCamelCase_  )  ):
									if j in dup_set:
												continue
									if _is_equal_tensor_proto(inits[i]  ,							inits[j]  ):
												dup_set.add(lowerCamelCase_  )
												dup_set.add(lowerCamelCase_  )

												SCREAMING_SNAKE_CASE_   : int					  =      inits[j].data_type
												SCREAMING_SNAKE_CASE_   : str					  =      numpy.prod(inits[j].dims  )
												if dtype == 1:
															mem_size *= 4
												elif dtype == 6:
															mem_size *= 4
												elif dtype == 7 or dtype == 11:
															mem_size *= 8
												else:
															print('unexpected data type: '  ,							lowerCamelCase_  )
												total_reduced_size += mem_size

												SCREAMING_SNAKE_CASE_   : int					  =      inits[i].name
												SCREAMING_SNAKE_CASE_   : List[Any]					  =      inits[j].name

												if name_i in dup_map:
															dup_map[name_i].append(lowerCamelCase_  )
												else:
															SCREAMING_SNAKE_CASE_   : Optional[int]					  =      [name_j]
												ind_to_replace.append((j, i)  )

			print('total reduced size: '  ,							total_reduced_size / 10_24 / 10_24 / 10_24  ,							'GB'  )

			SCREAMING_SNAKE_CASE_   : int					  =      sorted(lowerCamelCase_  )
			_remove_dup_initializers_from_model(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'optimized_' + model_file_name
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      os.path.join(lowerCamelCase_  ,							lowerCamelCase_  )
			onnx.save(lowerCamelCase_  ,							lowerCamelCase_  )

			return new_model



