





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)

__lowerCamelCase	:					Optional[Any]			  =					_symbol_database.Default()


__lowerCamelCase	:					Optional[int]			  =					_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"""
)

__lowerCamelCase	:					str			  =					globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
    __lowerCamelCase	:					int			  =					None
    __lowerCamelCase	:					int			  =					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"
    __lowerCamelCase	:					Any			  =					45
    __lowerCamelCase	:					Any			  =					1581
    __lowerCamelCase	:					Dict			  =					1517
    __lowerCamelCase	:					Optional[int]			  =					1570
    __lowerCamelCase	:					int			  =					1584
    __lowerCamelCase	:					Tuple			  =					1793
    __lowerCamelCase	:					List[str]			  =					1795
    __lowerCamelCase	:					Optional[int]			  =					1916
    __lowerCamelCase	:					Any			  =					1864
    __lowerCamelCase	:					Tuple			  =					1905
    __lowerCamelCase	:					Optional[Any]			  =					1919
    __lowerCamelCase	:					Any			  =					2429
    __lowerCamelCase	:					List[Any]			  =					2208
    __lowerCamelCase	:					List[str]			  =					2418
    __lowerCamelCase	:					Union[str, Any]			  =					2323
    __lowerCamelCase	:					Dict			  =					2407
# @@protoc_insertion_point(module_scope)







from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_=None	,							A_=None	,							A_=0       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       1.0 if scale is None else scale
       UpperCamelCase     : Optional[int]	     =       0.0 if loc is None else loc

       super().__init__(A_	,							[AffineTransform(loc=self.loc	,							scale=self.scale	,							event_dim=A_       )]       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.base_dist.mean * self.scale + self.loc





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.base_dist.variance * self.scale**2





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.variance.sqrt()



class  A__       (	nn.Module      ):





      def __init__(			self	,							A_	,							A_	,							A_	,							**A_       ):
       '''simple docstring'''




       super().__init__(**A_       )
       UpperCamelCase     : Union[str, Any]	     =       args_dim
       UpperCamelCase     : str	     =       nn.ModuleList([nn.Linear(A_	,							A_       ) for dim in args_dim.values()]       )
       UpperCamelCase     : Union[str, Any]	     =       domain_map





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[Any]	     =       [proj(A_       ) for proj in self.proj]

       return self.domain_map(*A_       )



class  A__       (	nn.Module      ):





      def __init__(			self	,							A_       ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase     : str	     =       function





      def 	__UpperCamelCase(			self	,							A_	,							*A_       ):
       '''simple docstring'''




       return self.function(A_	,							*A_       )



class  A__       :
      _UpperCAmelCase     :type
      _UpperCAmelCase     :int
      _UpperCAmelCase     :Dict[str, int]





      def __init__(			self	,							A_ = 1       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       dim
       UpperCamelCase     : Union[str, Any]	     =       {k: dim * self.args_dim[k] for k in self.args_dim}





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if self.dim == 1:
        return self.distribution_class(*A_       )
       else:
        return Independent(self.distribution_class(*A_       )	,							1       )





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None	,							A_ = None	,							):
       '''simple docstring'''




       UpperCamelCase     : str	     =       self._base_distribution(A_       )
       if loc is None and scale is None:
        return distr
       else:
        return AffineTransformed(A_	,							loc=A_	,							scale=A_	,							event_dim=self.event_dim       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return () if self.dim == 1 else (self.dim,)





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return len(self.event_shape       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return 0.0





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       return ParameterProjection(
           in_features=A_	,							args_dim=self.args_dim	,							domain_map=LambdaLayer(self.domain_map       )	,							)





      def 	__UpperCamelCase(			self	,							*A_       ):
       '''simple docstring'''




       raise NotImplementedError()





      @staticmethod
      def 	__UpperCamelCase(			A_       ):
       '''simple docstring'''




       return (x + torch.sqrt(torch.square(A_       ) + 4.0       )) / 2.0



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict[str, int]						   =       {"df": 1, "loc": 1, "scale": 1}
      _UpperCAmelCase     :type						   =       StudentT





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[Any]	     =       cls.squareplus(A_       ).clamp_min(torch.finfo(scale.dtype       ).eps       )
       UpperCamelCase     : int	     =       2.0 + cls.squareplus(A_       )
       return df.squeeze(-1       ), loc.squeeze(-1       ), scale.squeeze(-1       )



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict[str, int]						   =       {"loc": 1, "scale": 1}
      _UpperCAmelCase     :type						   =       Normal





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       cls.squareplus(A_       ).clamp_min(torch.finfo(scale.dtype       ).eps       )
       return loc.squeeze(-1       ), scale.squeeze(-1       )



class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Dict[str, int]						   =       {"total_count": 1, "logits": 1}
      _UpperCAmelCase     :type						   =       NegativeBinomial





      @classmethod
      def 	__UpperCamelCase(			cls	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       cls.squareplus(A_       )
       return total_count.squeeze(-1       ), logits.squeeze(-1       )





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : Optional[int]	     =       distr_args
       if self.dim == 1:
        return self.distribution_class(total_count=A_	,							logits=A_       )
       else:
        return Independent(self.distribution_class(total_count=A_	,							logits=A_       )	,							1       )





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : Any	     =       distr_args

       if scale is not None:
        # See scaling property of Gamma.
        logits += scale.log()

       return self._base_distribution((total_count, logits)       )
