'''simple docstring'''





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Optional[Any]  ={'vocab_file': 'vocab.json'}

SCREAMING_SNAKE_CASE_:     int  ={
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

SCREAMING_SNAKE_CASE_:     Union[str, Any]  ={'mgp-str': 27}




class        __A      ( UpperCamelCase__	):
    a__       :						str          =  VOCAB_FILES_NAMES
    a__       :						List[Any]          =  PRETRAINED_VOCAB_FILES_MAP
    a__       :						Union[str, Any]          =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



    def __init__(self    :    str   ,			__a    :    Tuple   ,			__a    :    str="[GO]"   ,			__a    :    int="[GO]"   ,			__a    :    Tuple="[s]"   ,			__a    :    List[Any]="[GO]"   ,			**__a    :    Any ):
         super().__init__(
             unk_token=__a   ,			bos_token=__a   ,			eos_token=__a   ,			pad_token=__a   ,			**__a   ,			)

         with open(__a   ,			encoding="utf-8" ) as vocab_handle:
              UpperCAmelCase_           =   json.load(__a )
         UpperCAmelCase_           =   {v: k for k, v in self.vocab.items()}



    @property
    def 					_lowercase						(self    :    List[Any] ):
         return len(self.vocab )



    def 					_lowercase						(self    :    Dict ):
         return dict(self.vocab   ,			**self.added_tokens_encoder )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    Optional[Any] ):
         UpperCAmelCase_           =   []
         for s in text:
              char_tokens.extend(__a )
         return char_tokens



    def 					_lowercase						(self    :    str   ,			__a    :    str ):
         return self.vocab.get(__a   ,			self.vocab.get(self.unk_token ) )



    def 					_lowercase						(self    :    int   ,			__a    :    str ):
         return self.decoder.get(__a )






    def 					_lowercase						(self    :    Tuple   ,			__a    :    str   ,			__a    :    Optional[str] = None ):
         if not os.path.isdir(__a ):
              logger.error("Vocabulary path ({}) should be a directory".format(__a ) )
              return
         UpperCAmelCase_           =   os.path.join(
             __a   ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

         with open(__a   ,			"w"   ,			encoding="utf-8" ) as f:
              f.write(json.dumps(self.vocab   ,			indent=2   ,			sort_keys=__a   ,			ensure_ascii=__a ) + "\n" )

         return (vocab_file,)





'''simple docstring'''





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      ( UpperCamelCase__	):



    def __init__(self    :    int   ,			__a    :    Distribution   ,			__a    :    Dict=None   ,			__a    :    int=None   ,			__a    :    Any=0 ):
         UpperCAmelCase_           =   1.0 if scale is None else scale
         UpperCAmelCase_           =   0.0 if loc is None else loc

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



    @property
    def 					_lowercase						(self    :    Union[str, Any] ):
         return self.base_dist.mean * self.scale + self.loc



    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.base_dist.variance * self.scale**2






    @property
    def 					_lowercase						(self    :    List[Any] ):
         return self.variance.sqrt()




class        __A      ( nn.Module	):



    def __init__(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    Dict[str, int]   ,			__a    :    Callable[..., Tuple[torch.Tensor]]   ,			**__a    :    List[str] ):
         super().__init__(**__a )
         UpperCAmelCase_           =   args_dim
         UpperCAmelCase_           =   nn.ModuleList([nn.Linear(__a   ,			__a ) for dim in args_dim.values()] )
         UpperCAmelCase_           =   domain_map






    def 					_lowercase						(self    :    List[str]   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   [proj(__a ) for proj in self.proj]

         return self.domain_map(*__a )




class        __A      ( nn.Module	):



    def __init__(self    :    Union[str, Any]   ,			__a    :    List[str] ):
         super().__init__()
         UpperCAmelCase_           =   function






    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    List[str]   ,			*__a    :    Optional[int] ):
         return self.function(__a   ,			*__a )




class        __A      :
    a__       :						type
    a__       :						int
    a__       :						Dict[str, int]



    def __init__(self    :    List[Any]   ,			__a    :    int = 1 ):
         UpperCAmelCase_           =   dim
         UpperCAmelCase_           =   {k: dim * self.args_dim[k] for k in self.args_dim}



    def 					_lowercase						(self    :    Any   ,			__a    :    Any ):
         if self.dim == 1:
              return self.distribution_class(*__a )
         else:
              return Independent(self.distribution_class(*__a )   ,			1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    Union[str, Any]   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None   ,			):
         UpperCAmelCase_           =   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 					_lowercase						(self    :    Any ):
         return () if self.dim == 1 else (self.dim,)



    @property
    def 					_lowercase						(self    :    Dict ):
         return len(self.event_shape )



    @property
    def 					_lowercase						(self    :    Tuple ):
         return 0.0



    def 					_lowercase						(self    :    List[str]   ,			__a    :    int ):
         return ParameterProjection(
             in_features=__a   ,			args_dim=self.args_dim   ,			domain_map=LambdaLayer(self.domain_map )   ,			)



    def 					_lowercase						(self    :    Optional[int]   ,			*__a    :    torch.Tensor ):
         raise NotImplementedError()






    @staticmethod
    def 					_lowercase						(__a    :    torch.Tensor ):
         return (x + torch.sqrt(torch.square(__a ) + 4.0 )) / 2.0




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"df": 1, "loc": 1, "scale": 1}
    a__       :						type          =  StudentT



    @classmethod
    def 					_lowercase						(cls    :    Union[str, Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         UpperCAmelCase_           =   2.0 + cls.squareplus(__a )
         return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"loc": 1, "scale": 1}
    a__       :						type          =  Normal



    @classmethod
    def 					_lowercase						(cls    :    Tuple   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a ).clamp_min(torch.finfo(scale.dtype ).eps )
         return loc.squeeze(-1 ), scale.squeeze(-1 )




class        __A      ( UpperCamelCase__	):
    a__       :						Dict[str, int]          =  {"total_count": 1, "logits": 1}
    a__       :						type          =  NegativeBinomial



    @classmethod
    def 					_lowercase						(cls    :    Optional[Any]   ,			__a    :    torch.Tensor   ,			__a    :    torch.Tensor ):
         UpperCAmelCase_           =   cls.squareplus(__a )
         return total_count.squeeze(-1 ), logits.squeeze(-1 )



    def 					_lowercase						(self    :    List[str]   ,			__a    :    str ):
         UpperCAmelCase_    , UpperCAmelCase_           =   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 					_lowercase						(self    :    Optional[Any]   ,			__a    :    int   ,			__a    :    Optional[torch.Tensor] = None   ,			__a    :    Optional[torch.Tensor] = None ):
         UpperCAmelCase_    , UpperCAmelCase_           =   distr_args

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

         return self._base_distribution((total_count, logits) )




