


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        _lowercase      (			A__       ):






				'''simple docstring'''



				def __init__(       self			:List[str]			,			lowerCAmelCase__			:Distribution			,			lowerCAmelCase__			:str=None			,			lowerCAmelCase__			:Optional[Any]=None			,			lowerCAmelCase__			:Any=0	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		1.0 if scale is None else scale
										__SCREAMING_SNAKE_CASE						:     List[str]					=		0.0 if loc is None else loc

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


				@property
				def 		__magic_name__(       self			:List[Any]	)		->			Any:
										return self.base_dist.mean * self.scale + self.loc


				@property
				def 		__magic_name__(       self			:List[str]	)		->			List[Any]:
										return self.base_dist.variance * self.scale**2



				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			Optional[int]:
										return self.variance.sqrt()




class        _lowercase      (			nn.Module       ):






				'''simple docstring'''



				def __init__(       self			:Optional[Any]			,			lowerCAmelCase__			:int			,			lowerCAmelCase__			:Dict[str, int]			,			lowerCAmelCase__			:Callable[..., Tuple[torch.Tensor]]			,			**lowerCAmelCase__			:Tuple	)		->			None:
										super().__init__(**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		args_dim
										__SCREAMING_SNAKE_CASE						:     Tuple					=		nn.ModuleList([nn.Linear(lowerCAmelCase__			,			lowerCAmelCase__	) for dim in args_dim.values()]	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		domain_map



				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:torch.Tensor	)		->			Tuple[torch.Tensor]:
										__SCREAMING_SNAKE_CASE						:     str					=		[proj(lowerCAmelCase__	) for proj in self.proj]

										return self.domain_map(*lowerCAmelCase__	)




class        _lowercase      (			nn.Module       ):






				'''simple docstring'''



				def __init__(       self			:List[str]			,			lowerCAmelCase__			:Dict	)		->			Dict:
										super().__init__()
										__SCREAMING_SNAKE_CASE						:     int					=		function



				def 		__magic_name__(       self			:int			,			lowerCAmelCase__			:Optional[int]			,			*lowerCAmelCase__			:Optional[int]	)		->			Optional[Any]:
										return self.function(lowerCAmelCase__			,			*lowerCAmelCase__	)




class        _lowercase      :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			type
				SCREAMING_SNAKE_CASE__				:			int
				SCREAMING_SNAKE_CASE__				:			Dict[str, int]


				def __init__(       self			:List[str]			,			lowerCAmelCase__			:int = 1	)		->			None:
										__SCREAMING_SNAKE_CASE						:     str					=		dim
										__SCREAMING_SNAKE_CASE						:     str					=		{k: dim * self.args_dim[k] for k in self.args_dim}


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:int	)		->			Dict:
										if self.dim == 1:
																return self.distribution_class(*lowerCAmelCase__	)
										else:
																return Independent(self.distribution_class(*lowerCAmelCase__	)			,			1	)


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:Dict			,			lowerCAmelCase__			:Optional[torch.Tensor] = None			,			lowerCAmelCase__			:Optional[torch.Tensor] = None			,			)		->			Distribution:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self._base_distribution(lowerCAmelCase__	)
										if loc is None and scale is None:
																return distr
										else:
																return AffineTransformed(lowerCAmelCase__			,			loc=lowerCAmelCase__			,			scale=lowerCAmelCase__			,			event_dim=self.event_dim	)


				@property
				def 		__magic_name__(       self			:int	)		->			Tuple:
										return () if self.dim == 1 else (self.dim,)


				@property
				def 		__magic_name__(       self			:int	)		->			int:
										return len(self.event_shape	)


				@property
				def 		__magic_name__(       self			:Union[str, Any]	)		->			float:
										return 0.0


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:int	)		->			nn.Module:
										return ParameterProjection(
										    in_features=lowerCAmelCase__			,			args_dim=self.args_dim			,			domain_map=LambdaLayer(self.domain_map	)			,			)


				def 		__magic_name__(       self			:int			,			*lowerCAmelCase__			:torch.Tensor	)		->			List[str]:
										raise NotImplementedError()



				@staticmethod
				def 		__magic_name__(       lowerCAmelCase__			:torch.Tensor	)		->			torch.Tensor:
										return (x + torch.sqrt(torch.square(lowerCAmelCase__	) + 4.0	)) / 2.0




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict[str, int]						=		{"df": 1, "loc": 1, "scale": 1}
				SCREAMING_SNAKE_CASE__				:			type						=		StudentT


				@classmethod
				def 		__magic_name__(       cls			:List[Any]			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		cls.squareplus(lowerCAmelCase__	).clamp_min(torch.finfo(scale.dtype	).eps	)
										__SCREAMING_SNAKE_CASE						:     str					=		2.0 + cls.squareplus(lowerCAmelCase__	)
										return df.squeeze(-1	), loc.squeeze(-1	), scale.squeeze(-1	)




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict[str, int]						=		{"loc": 1, "scale": 1}
				SCREAMING_SNAKE_CASE__				:			type						=		Normal


				@classmethod
				def 		__magic_name__(       cls			:Union[str, Any]			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     Dict					=		cls.squareplus(lowerCAmelCase__	).clamp_min(torch.finfo(scale.dtype	).eps	)
										return loc.squeeze(-1	), scale.squeeze(-1	)




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict[str, int]						=		{"total_count": 1, "logits": 1}
				SCREAMING_SNAKE_CASE__				:			type						=		NegativeBinomial


				@classmethod
				def 		__magic_name__(       cls			:List[str]			,			lowerCAmelCase__			:torch.Tensor			,			lowerCAmelCase__			:torch.Tensor	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		cls.squareplus(lowerCAmelCase__	)
										return total_count.squeeze(-1	), logits.squeeze(-1	)


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:Optional[int]	)		->			Distribution:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Dict					=		distr_args
										if self.dim == 1:
																return self.distribution_class(total_count=lowerCAmelCase__			,			logits=lowerCAmelCase__	)
										else:
																return Independent(self.distribution_class(total_count=lowerCAmelCase__			,			logits=lowerCAmelCase__	)			,			1	)



				def 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:Optional[torch.Tensor] = None			,			lowerCAmelCase__			:Optional[torch.Tensor] = None	)		->			Distribution:
										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		distr_args

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

										return self._base_distribution((total_count, logits)	)








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{'vocab_file': 'vocab.txt'}

__UpperCAmelCase								=						{
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

__UpperCAmelCase								=						{
    'YituTech/conv-bert-base': 512,
    'YituTech/conv-bert-medium-small': 512,
    'YituTech/conv-bert-small': 512,
}


__UpperCAmelCase								=						{
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  VOCAB_FILES_NAMES
		_snake_case    :    int       						=  PRETRAINED_VOCAB_FILES_MAP
		_snake_case    :    Dict       						=  PRETRAINED_INIT_CONFIGURATION
		_snake_case    :    List[str]       						=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_snake_case    :    Any       						=  ConvBertTokenizer



		def __init__(							self				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase=True				,		_UpperCamelCase="[UNK]"				,		_UpperCamelCase="[SEP]"				,		_UpperCamelCase="[PAD]"				,		_UpperCamelCase="[CLS]"				,		_UpperCamelCase="[MASK]"				,		_UpperCamelCase=True				,		_UpperCamelCase=None				,		**_UpperCamelCase				,		)	->							Dict:
							super().__init__(
							    _UpperCamelCase				,		tokenizer_file=_UpperCamelCase				,		do_lower_case=_UpperCamelCase				,		unk_token=_UpperCamelCase				,		sep_token=_UpperCamelCase				,		pad_token=_UpperCamelCase				,		cls_token=_UpperCamelCase				,		mask_token=_UpperCamelCase				,		tokenize_chinese_chars=_UpperCamelCase				,		strip_accents=_UpperCamelCase				,		**_UpperCamelCase				,		)

							UpperCAmelCase_    :      Optional[int]            =						json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
							if (
							    normalizer_state.get('lowercase'				,		_UpperCamelCase   ) != do_lower_case
							    or normalizer_state.get('strip_accents'				,		_UpperCamelCase   ) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'				,		_UpperCamelCase   ) != tokenize_chinese_chars
							):
												UpperCAmelCase_    :      Any            =						getattr(_UpperCamelCase				,		normalizer_state.pop('type'   )   )
												UpperCAmelCase_    :      str            =						do_lower_case
												UpperCAmelCase_    :      List[Any]            =						strip_accents
												UpperCAmelCase_    :      str            =						tokenize_chinese_chars
												UpperCAmelCase_    :      Tuple            =						normalizer_class(**_UpperCamelCase   )

							UpperCAmelCase_    :      Any            =						do_lower_case



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							List[str]:
							UpperCAmelCase_    :      int            =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

							return output



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							List[int]:
							UpperCAmelCase_    :      Union[str, Any]            =						[self.sep_token_id]
							UpperCAmelCase_    :      int            =						[self.cls_token_id]
							if token_ids_a is None:
												return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None   )	->							Tuple[str]:
							UpperCAmelCase_    :      Any            =						self._tokenizer.model.save(_UpperCamelCase				,		name=_UpperCamelCase   )
							return tuple(_UpperCamelCase   )






