



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  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=None	,__lowerCamelCase=None	,__lowerCamelCase=0				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  1.0 if scale is None else scale
						lowerCAmelCase__  :       Optional[int]				=  0.0 if loc is None else loc

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



		@property
		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






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



		@property
		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






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



		@property
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						return self.variance.sqrt()



class  lowerCamelCase__    (							nn.Module):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase				)  ->    None:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  args_dim
						lowerCAmelCase__  :       Dict				=  nn.ModuleList([nn.Linear(__lowerCamelCase	,__lowerCamelCase				) for dim in args_dim.values()]				)
						lowerCAmelCase__  :       List[str]				=  domain_map



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Tuple[torch.Tensor]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  [proj(__lowerCamelCase				) for proj in self.proj]

						return self.domain_map(*__lowerCamelCase				)



class  lowerCamelCase__    (							nn.Module):




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__  :       Optional[int]				=  function



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,*__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						return self.function(__lowerCamelCase	,*__lowerCamelCase				)



class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=42
		snake_case_			=42
		snake_case_			=42



		def __init__(self	,__lowerCamelCase = 1				)  ->    None:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  dim
						lowerCAmelCase__  :       int				=  {k: dim * self.args_dim[k] for k in self.args_dim}



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None	,)  ->    Distribution:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  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 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






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



		@property
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						return len(self.event_shape				)



		@property
		def 	lowerCAmelCase__   (self				)  ->    float:

						"""simple docstring"""






						return 0.0



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    nn.Module:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,*__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						raise NotImplementedError()



		@staticmethod
		def 	lowerCAmelCase__   (__lowerCamelCase				)  ->    torch.Tensor:

						"""simple docstring"""






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



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			={"""df""": 1, """loc""": 1, """scale""": 1}
		snake_case_			=StudentT



		@classmethod
		def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  cls.squareplus(__lowerCamelCase				).clamp_min(torch.finfo(scale.dtype				).eps				)
						lowerCAmelCase__  :       str				=  2.0 + cls.squareplus(__lowerCamelCase				)
						return df.squeeze(-1				), loc.squeeze(-1				), scale.squeeze(-1				)



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			={"""loc""": 1, """scale""": 1}
		snake_case_			=Normal



		@classmethod
		def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  cls.squareplus(__lowerCamelCase				).clamp_min(torch.finfo(scale.dtype				).eps				)
						return loc.squeeze(-1				), scale.squeeze(-1				)



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			={"""total_count""": 1, """logits""": 1}
		snake_case_			=NegativeBinomial



		@classmethod
		def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  cls.squareplus(__lowerCamelCase				)
						return total_count.squeeze(-1				), logits.squeeze(-1				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Distribution:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  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 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None				)  ->    Distribution:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  distr_args

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

						return self._base_distribution((total_count, logits)				)






import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case       :	List[Any]            =logging.get_logger(__name__)

__snake_case       :	str            ={
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""mvp"""
		snake_case_			=["""past_key_values"""]
		snake_case_			={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(self	,__lowerCamelCase=5_02_67	,__lowerCamelCase=10_24	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase="gelu"	,__lowerCamelCase=10_24	,__lowerCamelCase=0.1	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase=0.02	,__lowerCamelCase=0.0	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=1	,__lowerCamelCase=0	,__lowerCamelCase=2	,__lowerCamelCase=True	,__lowerCamelCase=2	,__lowerCamelCase=2	,__lowerCamelCase=False	,__lowerCamelCase=1_00	,__lowerCamelCase=8_00	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  vocab_size
						lowerCAmelCase__  :       Dict				=  max_position_embeddings
						lowerCAmelCase__  :       Optional[int]				=  d_model
						lowerCAmelCase__  :       Any				=  encoder_ffn_dim
						lowerCAmelCase__  :       str				=  encoder_layers
						lowerCAmelCase__  :       Union[str, Any]				=  encoder_attention_heads
						lowerCAmelCase__  :       Dict				=  decoder_ffn_dim
						lowerCAmelCase__  :       Optional[int]				=  decoder_layers
						lowerCAmelCase__  :       int				=  decoder_attention_heads
						lowerCAmelCase__  :       str				=  dropout
						lowerCAmelCase__  :       int				=  attention_dropout
						lowerCAmelCase__  :       Optional[Any]				=  activation_dropout
						lowerCAmelCase__  :       Optional[int]				=  activation_function
						lowerCAmelCase__  :       int				=  init_std
						lowerCAmelCase__  :       List[Any]				=  encoder_layerdrop
						lowerCAmelCase__  :       Union[str, Any]				=  decoder_layerdrop
						lowerCAmelCase__  :       Tuple				=  classifier_dropout
						lowerCAmelCase__  :       Optional[int]				=  use_cache
						lowerCAmelCase__  :       List[Any]				=  encoder_layers
						lowerCAmelCase__  :       List[str]				=  scale_embedding  # scale factor will be sqrt(d_model) if True
						lowerCAmelCase__  :       Any				=  use_prompt
						lowerCAmelCase__  :       Optional[Any]				=  prompt_length
						lowerCAmelCase__  :       int				=  prompt_mid_dim

						super().__init__(
						    pad_token_id=__lowerCamelCase	,bos_token_id=__lowerCamelCase	,eos_token_id=__lowerCamelCase	,is_encoder_decoder=__lowerCamelCase	,decoder_start_token_id=__lowerCamelCase	,forced_eos_token_id=__lowerCamelCase	,**__lowerCamelCase	,)

						if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  self.bos_token_id
										warnings.warn(
										    f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
										    '''The config can simply be saved and uploaded again to be fixed.'''				)

