


"""simple docstring"""




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


		'''simple docstring'''



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

				__a           =			n_token
				__a           =			d_embed
				__a           =			d_proj

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

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

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

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

				__a           =			keep_order

		def 			__UpperCAmelCase       (  self							, _a							, _a							, _a							, _a      ):
				if proj is None:
						__a           =			nn.functional.linear(_a							, _a							, bias=_a      )
				else:
						# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
						__a           =			nn.functional.linear(_a							, proj.t().contiguous()      )
						__a           =			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							, _a							, _a=None							, _a=False      ):

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

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

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

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

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

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

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

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

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

										if indices_i.numel() == 0:
												continue

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

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

										__a           =			self._compute_logit(_a							, _a							, _a							, _a      )
										__a           =			nn.functional.log_softmax(_a							, dim=1      )
										__a           =			self.cutoffs[0] + i - 1  # No probability for the head cluster
										if labels is not None:
												__a           =			head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
												    1							, target_i[:, None]      ).squeeze(1      )
										else:
												__a           =			head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
												__a           =			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							, _a      ):
				if self.n_clusters == 0:
						__a           =			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
						__a      ,     __a           =			[], []
						for i in range(len(self.cutoffs      )      ):
								if self.div_val == 1:
										__a      ,     __a           =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
										__a           =			self.out_layers[0].weight[l_idx:r_idx]
										__a           =			self.out_layers[0].bias[l_idx:r_idx]
								else:
										__a           =			self.out_layers[i].weight
										__a           =			self.out_layers[i].bias

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

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

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

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

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

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

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

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

						return out













import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Any       			=      BioGptTokenizer
			lowerCamelCase_     :     Optional[Any]       			=      False






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							snake_case_						:    Optional[Any]				=  [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''w</w>''',
							    '''r</w>''',
							    '''t</w>''',
							    '''lo''',
							    '''low''',
							    '''er</w>''',
							    '''low</w>''',
							    '''lowest</w>''',
							    '''newer</w>''',
							    '''wider</w>''',
							    '''<unk>''',
							]
							snake_case_						:    Union[str, Any]				=  dict(zip(__magic_name__					,							range(len(__magic_name__    )    )    )    )
							snake_case_						:    Union[str, Any]				=  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

							snake_case_						:    Any				=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['''vocab_file''']    )
							snake_case_						:    Dict				=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['''merges_file''']    )
							with open(self.vocab_file					,							'''w'''    ) as fp:
											fp.write(json.dumps(__magic_name__    )    )
							with open(self.merges_file					,							'''w'''    ) as fp:
											fp.write('''\n'''.join(__magic_name__    )    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    str				=  '''lower newer'''
							snake_case_						:    Dict				=  '''lower newer'''
							return input_text, output_text






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  BioGptTokenizer(self.vocab_file					,							self.merges_file    )

							snake_case_						:    Union[str, Any]				=  '''lower'''
							snake_case_						:    Optional[int]				=  ['''low''', '''er</w>''']
							snake_case_						:    Any				=  tokenizer.tokenize(__magic_name__    )
							self.assertListEqual(__magic_name__					,							__magic_name__    )

							snake_case_						:    Optional[int]				=  tokens + ['''<unk>''']
							snake_case_						:    List[str]				=  [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__    )					,							__magic_name__    )






			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''    )

							snake_case_						:    List[Any]				=  tokenizer.encode('''sequence builders'''					,							add_special_tokens=__magic_name__    )
							snake_case_						:    str				=  tokenizer.encode('''multi-sequence build'''					,							add_special_tokens=__magic_name__    )

							snake_case_						:    str				=  tokenizer.build_inputs_with_special_tokens(__magic_name__    )
							snake_case_						:    List[str]				=  tokenizer.build_inputs_with_special_tokens(__magic_name__					,							__magic_name__    )

							self.assertTrue(encoded_sentence == [2] + text    )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a    )
