'''simple docstring'''

from typing import Callable, Optional, Union

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


_A      :				Tuple 				=    logging.get_logger(__name__)

_A      :				Any 				=    {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}







class     _lowercase					(    __lowerCamelCase      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	List[Any]				     = "prophetnet"
 _SCREAMING_SNAKE_CASE					:	Dict				     = ["past_key_values"]
 _SCREAMING_SNAKE_CASE					:	Any				     = {
     "num_attention_heads": "num_encoder_attention_heads",
 }
 def __init__(				self       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.1				,	SCREAMING_SNAKE_CASE__       :     Optional[Union[str, Callable]] = "gelu"				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 3_05_22				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 10_24				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 40_96				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 12				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 16				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 40_96				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 12				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 16				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.1				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.1				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 5_12				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.0_2				,	SCREAMING_SNAKE_CASE__       :     Optional[bool] = True				,	SCREAMING_SNAKE_CASE__       :     Optional[bool] = True				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 0				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 2				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 32				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 1_28				,	SCREAMING_SNAKE_CASE__       :     Optional[bool] = False				,	SCREAMING_SNAKE_CASE__       :     Optional[float] = 0.0				,	SCREAMING_SNAKE_CASE__       :     Optional[bool] = True				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 0				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 1				,	SCREAMING_SNAKE_CASE__       :     Optional[int] = 2				,	**SCREAMING_SNAKE_CASE__       :     str				,	)  ->   List[str]:
  __lowerCAmelCase    							=						vocab_size
  __lowerCAmelCase    							=						hidden_size
  __lowerCAmelCase    							=						encoder_ffn_dim
  __lowerCAmelCase    							=						num_encoder_layers
  __lowerCAmelCase    							=						num_encoder_attention_heads
  __lowerCAmelCase    							=						decoder_ffn_dim
  __lowerCAmelCase    							=						num_decoder_layers
  __lowerCAmelCase    							=						num_decoder_attention_heads
  __lowerCAmelCase    							=						max_position_embeddings
  __lowerCAmelCase    							=						init_std  # Normal(0, this parameter)
  __lowerCAmelCase    							=						activation_function

  # parameters for prophetnet
  __lowerCAmelCase    							=						ngram
  __lowerCAmelCase    							=						num_buckets
  __lowerCAmelCase    							=						relative_max_distance
  __lowerCAmelCase    							=						disable_ngram_loss
  __lowerCAmelCase    							=						eps

  # 3 Types of Dropout
  __lowerCAmelCase    							=						attention_dropout
  __lowerCAmelCase    							=						activation_dropout
  __lowerCAmelCase    							=						dropout

  __lowerCAmelCase    							=						use_cache

  super().__init__(
      pad_token_id=_UpperCamelCase				,	bos_token_id=_UpperCamelCase				,	eos_token_id=_UpperCamelCase				,	is_encoder_decoder=_UpperCamelCase				,	add_cross_attention=_UpperCamelCase				,	decoder_start_token_id=_UpperCamelCase				,	**_UpperCamelCase				,	)
 @property
 def a (				self       :     int							)  ->   int:
  return self.num_encoder_layers + self.num_decoder_layers





 @num_hidden_layers.setter
 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     int							)  ->   Union[str, Any]:
  raise NotImplementedError(
      """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
      """ `num_decoder_layers`."""							)







'''simple docstring'''

import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class     _lowercase					(    nn.Linear      ):


 '''simple docstring'''






 def __init__(				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	SCREAMING_SNAKE_CASE__       :     str = "constant"				,	SCREAMING_SNAKE_CASE__       :     float = 0.0				,	SCREAMING_SNAKE_CASE__       :     str = "topK"				,	)  ->   Any:
  super(SCREAMING_SNAKE_CASE__				,	self							).__init__(in_features=SCREAMING_SNAKE_CASE__				,	out_features=SCREAMING_SNAKE_CASE__				,	bias=SCREAMING_SNAKE_CASE__							)
  assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
  __lowerCAmelCase    							=						pruning_method

  if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
   __lowerCAmelCase    							=						mask_scale
   __lowerCAmelCase    							=						mask_init
   __lowerCAmelCase    							=						nn.Parameter(torch.empty(self.weight.size()							)							)
   self.init_mask()
 def a (				self       :     Union[str, Any]							)  ->   int:
  if self.mask_init == "constant":
   init.constant_(self.mask_scores				,	val=self.mask_scale							)
  elif self.mask_init == "uniform":
   init.uniform_(self.mask_scores				,	a=-self.mask_scale				,	b=self.mask_scale							)
  elif self.mask_init == "kaiming":
   init.kaiming_uniform_(self.mask_scores				,	a=math.sqrt(5							)							)





 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     torch.tensor				,	SCREAMING_SNAKE_CASE__       :     float							)  ->   Tuple:
  # Get the mask
  if self.pruning_method == "topK":
   __lowerCAmelCase    							=						TopKBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
   __lowerCAmelCase    							=						"""sigmoied""" in self.pruning_method
   __lowerCAmelCase    							=						ThresholdBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "magnitude":
   __lowerCAmelCase    							=						MagnitudeBinarizer.apply(self.weight				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "l0":
   __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						-0.1, 1.1, 2 / 3
   if self.training:
    __lowerCAmelCase    							=						torch.zeros_like(self.mask_scores							).uniform_().clamp(0.0_0_0_1				,	0.9_9_9_9							)
    __lowerCAmelCase    							=						torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b							)
   else:
    __lowerCAmelCase    							=						torch.sigmoid(self.mask_scores							)
   __lowerCAmelCase    							=						s * (r - l) + l
   __lowerCAmelCase    							=						s_bar.clamp(min=0.0				,	max=1.0							)
  # Mask weights with computed mask
  __lowerCAmelCase    							=						mask * self.weight
  # Compute output (linear layer) with masked weights
  return nn.functional.linear(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	self.bias							)






