


'''simple docstring'''


from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

UpperCAmelCase_						:  Optional[int]				  =						_symbol_database.Default()


UpperCAmelCase_						:  Any				  =						_descriptor_pool.Default().AddSerializedFile(
    B'\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

UpperCAmelCase_						:  Tuple				  =						globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
				UpperCAmelCase_						:  List[Any]				  =						None
				UpperCAmelCase_						:  Any				  =						b"""H\003"""
				# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
				# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
				# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
				# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
				# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
				UpperCAmelCase_						:  Union[str, Any]				  =						45
				UpperCAmelCase_						:  Optional[int]				  =						1581
				UpperCAmelCase_						:  int				  =						1517
				UpperCAmelCase_						:  List[Any]				  =						1570
				UpperCAmelCase_						:  Dict				  =						1584
				UpperCAmelCase_						:  List[str]				  =						1793
				UpperCAmelCase_						:  List[str]				  =						1795
				UpperCAmelCase_						:  List[Any]				  =						1916
				UpperCAmelCase_						:  List[str]				  =						1864
				UpperCAmelCase_						:  int				  =						1905
				UpperCAmelCase_						:  str				  =						1919
				UpperCAmelCase_						:  Any				  =						2429
				UpperCAmelCase_						:  Optional[Any]				  =						2208
				UpperCAmelCase_						:  Optional[Any]				  =						2418
				UpperCAmelCase_						:  str				  =						2323
				UpperCAmelCase_						:  Tuple				  =						2407
# @@protoc_insertion_point(module_scope)











import torch
from torch import nn



class 					__snake_case	(  nn.Module    ):


    '''simple docstring'''







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





        '''simple docstring'''

        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(A_   ,				A_     )     )     )
                else:
                    self.out_projs.append(A_     )

            self.out_layers.append(nn.Linear(A_   ,				A_     )     )
        else:
            for i in range(len(self.cutoffs     )     ):
                SCREAMING_SNAKE_CASE__				,		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(A_   ,				A_     )     )     )

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

        SCREAMING_SNAKE_CASE__											=			keep_order



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        if proj is None:
            SCREAMING_SNAKE_CASE__											=			nn.functional.linear(A_   ,				A_   ,				bias=A_     )
        else:
            # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
            SCREAMING_SNAKE_CASE__											=			nn.functional.linear(A_   ,				proj.t().contiguous()     )
            SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self   ,				A_   ,				A_=None   ,				A_=False     ):





        '''simple docstring'''

        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(A_   ,				self.out_layers[0].weight   ,				self.out_layers[0].bias   ,				self.out_projs[0]     )
            if labels is not None:
                SCREAMING_SNAKE_CASE__											=			labels != -1_00
                SCREAMING_SNAKE_CASE__											=			torch.zeros_like(A_   ,				dtype=hidden.dtype   ,				device=hidden.device     )
                SCREAMING_SNAKE_CASE__											=			(
                    -nn.functional.log_softmax(A_   ,				dim=-1     )[mask].gather(1   ,				labels[mask].unsqueeze(1     )     ).squeeze(1     )
                )
            else:
                SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				dim=-1     )
        else:
            # construct weights and biases
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			[], []
            for i in range(len(self.cutoffs     )     ):
                if self.div_val == 1:
                    SCREAMING_SNAKE_CASE__				,		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(A_     )
                biases.append(A_     )

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

            SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )
            SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				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(A_   ,				dtype=hidden.dtype   ,				device=hidden.device     )

            SCREAMING_SNAKE_CASE__											=			0
            SCREAMING_SNAKE_CASE__											=			[0] + self.cutoffs
            for i in range(len(A_     ) - 1     ):
                SCREAMING_SNAKE_CASE__				,		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   ,				A_     ) - l_idx
                    SCREAMING_SNAKE_CASE__											=			head_logprob.index_select(0   ,				A_     )
                    SCREAMING_SNAKE_CASE__											=			hidden.index_select(0   ,				A_     )
                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__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[i], biases[i], self.out_projs[i]

                    SCREAMING_SNAKE_CASE__											=			self._compute_logit(A_   ,				A_   ,				A_   ,				A_     )
                    SCREAMING_SNAKE_CASE__											=			nn.functional.log_softmax(A_   ,				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   ,				A_   ,				-logprob_i     )
                    else:
                        out[offset : offset + logprob_i.size(0     )].copy_(-logprob_i     )
                    offset += logprob_i.size(0     )

        return out




    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        if self.n_clusters == 0:
            SCREAMING_SNAKE_CASE__											=			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
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			[], []
            for i in range(len(self.cutoffs     )     ):
                if self.div_val == 1:
                    SCREAMING_SNAKE_CASE__				,		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(A_     )
                biases.append(A_     )

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

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

            SCREAMING_SNAKE_CASE__											=			[0] + self.cutoffs
            for i in range(len(A_     ) - 1     ):
                SCREAMING_SNAKE_CASE__				,		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__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			weights[i], biases[i], self.out_projs[i]

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

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

            return out
