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						(					snake_case_       ):

 def __init__( self	:							int   ,      snake_case	:							Distribution   ,      snake_case	:							Optional[int]=None   ,      snake_case	:							Union[str, Any]=None   ,      snake_case	:							Any=0			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					1.0 if scale is None else scale
  UpperCamelCase_					:						int =					0.0 if loc is None else loc

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

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Union[str, Any]:

  """simple docstring"""





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

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

  """simple docstring"""





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


 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Any:

  """simple docstring"""





  return self.variance.sqrt()





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Dict   ,      snake_case	:							int   ,      snake_case	:							Dict[str, int]   ,      snake_case	:							Callable[..., Tuple[torch.Tensor]]   ,      **snake_case	:							List[str]			)   ->						None:

  """simple docstring"""





  super().__init__(**snake_case			)
  UpperCamelCase_					:						Optional[Any] =					args_dim
  UpperCamelCase_					:						Optional[int] =					nn.ModuleList([nn.Linear(snake_case   ,      snake_case			) for dim in args_dim.values()]			)
  UpperCamelCase_					:						Any =					domain_map


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							torch.Tensor			)   ->						Tuple[torch.Tensor]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					[proj(snake_case			) for proj in self.proj]

  return self.domain_map(*snake_case			)





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Dict   ,      snake_case	:							Optional[int]			)   ->						List[str]:

  """simple docstring"""





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


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Union[str, Any]   ,      *snake_case	:							Tuple			)   ->						Tuple:

  """simple docstring"""





  return self.function(snake_case   ,      *snake_case			)





class 		_lowercase						:
 lowercase				     =   42
 lowercase				     =   42
 lowercase				     =   42

 def __init__( self	:							Optional[int]   ,      snake_case	:							int = 1			)   ->						None:

  """simple docstring"""





  UpperCamelCase_					:						Any =					dim
  UpperCamelCase_					:						Dict =					{k: dim * self.args_dim[k] for k in self.args_dim}

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							List[str]			)   ->						Dict:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							List[Any]   ,      snake_case	:							Optional[torch.Tensor] = None   ,      snake_case	:							Optional[torch.Tensor] = None   ,      )   ->						Distribution:

  """simple docstring"""





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

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Tuple:

  """simple docstring"""





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

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

  """simple docstring"""





  return len(self.event_shape			)

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

  """simple docstring"""





  return 0.0

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							int			)   ->						nn.Module:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      *snake_case	:							torch.Tensor			)   ->						Any:

  """simple docstring"""





  raise NotImplementedError()


 @staticmethod
 def 						SCREAMING_SNAKE_CASE__   ( snake_case	:							torch.Tensor			)   ->						torch.Tensor:

  """simple docstring"""





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





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   {"df": 1, "loc": 1, "scale": 1}
 lowercase				     =   StudentT

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							int   ,      snake_case	:							torch.Tensor   ,      snake_case	:							torch.Tensor   ,      snake_case	:							torch.Tensor			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					cls.squareplus(snake_case			).clamp_min(torch.finfo(scale.dtype			).eps			)
  UpperCamelCase_					:						Union[str, Any] =					2.0 + cls.squareplus(snake_case			)
  return df.squeeze(-1			), loc.squeeze(-1			), scale.squeeze(-1			)





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   {"loc": 1, "scale": 1}
 lowercase				     =   Normal

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							Dict   ,      snake_case	:							torch.Tensor   ,      snake_case	:							torch.Tensor			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					cls.squareplus(snake_case			).clamp_min(torch.finfo(scale.dtype			).eps			)
  return loc.squeeze(-1			), scale.squeeze(-1			)





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   {"total_count": 1, "logits": 1}
 lowercase				     =   NegativeBinomial

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							str   ,      snake_case	:							torch.Tensor   ,      snake_case	:							torch.Tensor			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Any =					cls.squareplus(snake_case			)
  return total_count.squeeze(-1			), logits.squeeze(-1			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Optional[Any]			)   ->						Distribution:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						List[str] =					distr_args
  if self.dim == 1:
   return self.distribution_class(total_count=snake_case   ,      logits=snake_case			)
  else:
   return Independent(self.distribution_class(total_count=snake_case   ,      logits=snake_case			)   ,      1			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							List[str]   ,      snake_case	:							Optional[torch.Tensor] = None   ,      snake_case	:							Optional[torch.Tensor] = None			)   ->						Distribution:

  """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)			)




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


a_    					=       logging.get_logger(__name__)

a_    					=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   'megatron-bert'

 def __init__( self	:							List[str]   ,      snake_case	:							Tuple=2_9_0_5_6   ,      snake_case	:							Dict=1_0_2_4   ,      snake_case	:							Dict=2_4   ,      snake_case	:							Union[str, Any]=1_6   ,      snake_case	:							Optional[int]=4_0_9_6   ,      snake_case	:							Optional[int]="gelu"   ,      snake_case	:							Any=0.1   ,      snake_case	:							Tuple=0.1   ,      snake_case	:							Optional[int]=5_1_2   ,      snake_case	:							List[Any]=2   ,      snake_case	:							Tuple=0.02   ,      snake_case	:							Optional[Any]=1e-12   ,      snake_case	:							str=0   ,      snake_case	:							Optional[int]="absolute"   ,      snake_case	:							Union[str, Any]=True   ,      **snake_case	:							Any   ,      )   ->						List[Any]:

  """simple docstring"""





  super().__init__(pad_token_id=snake_case   ,      **snake_case			)

  UpperCamelCase_					:						Optional[Any] =					vocab_size
  UpperCamelCase_					:						Any =					hidden_size
  UpperCamelCase_					:						Union[str, Any] =					num_hidden_layers
  UpperCamelCase_					:						List[Any] =					num_attention_heads
  UpperCamelCase_					:						str =					hidden_act
  UpperCamelCase_					:						List[str] =					intermediate_size
  UpperCamelCase_					:						List[Any] =					hidden_dropout_prob
  UpperCamelCase_					:						Any =					attention_probs_dropout_prob
  UpperCamelCase_					:						Tuple =					max_position_embeddings
  UpperCamelCase_					:						Dict =					type_vocab_size
  UpperCamelCase_					:						Optional[int] =					initializer_range
  UpperCamelCase_					:						Optional[Any] =					layer_norm_eps
  UpperCamelCase_					:						Dict =					position_embedding_type
  UpperCamelCase_					:						List[str] =					use_cache



