
"""simple docstring"""


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class    __lowerCamelCase     (   nn.Linear ):




				def __init__(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = True       ,	UpperCAmelCase = "constant"       ,	UpperCAmelCase = 0.0       ,	UpperCAmelCase = "topK"       ,	):
							super(UpperCAmelCase       ,	self      ).__init__(in_features=UpperCAmelCase       ,	out_features=UpperCAmelCase       ,	bias=UpperCAmelCase      )
							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 					UpperCAmelCase__	(							self      ):
							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 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							# Get the mask
							if self.pruning_method == "topK":
										lowerCamelCase_ 		=     TopKBinarizer.apply(self.mask_scores       ,	UpperCAmelCase      )
							elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
										lowerCamelCase_ 		=     '''sigmoied''' in self.pruning_method
										lowerCamelCase_ 		=     ThresholdBinarizer.apply(self.mask_scores       ,	UpperCAmelCase       ,	UpperCAmelCase      )
							elif self.pruning_method == "magnitude":
										lowerCamelCase_ 		=     MagnitudeBinarizer.apply(self.weight       ,	UpperCAmelCase      )
							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(UpperCAmelCase       ,	UpperCAmelCase       ,	self.bias      )





"""simple docstring"""


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


A_						       =				logging.get_logger(__name__)

A_						       =				{
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Dict									=						'yoso'




				def __init__(							self       ,	UpperCAmelCase=5_0265       ,	UpperCAmelCase=768       ,	UpperCAmelCase=12       ,	UpperCAmelCase=12       ,	UpperCAmelCase=3072       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=4096       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0.0_2       ,	UpperCAmelCase=1e-1_2       ,	UpperCAmelCase="absolute"       ,	UpperCAmelCase=True       ,	UpperCAmelCase=9       ,	UpperCAmelCase=64       ,	UpperCAmelCase=None       ,	UpperCAmelCase=True       ,	UpperCAmelCase=True       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0       ,	UpperCAmelCase=2       ,	**UpperCAmelCase       ,	):
							super().__init__(pad_token_id=UpperCAmelCase       ,	bos_token_id=UpperCAmelCase       ,	eos_token_id=UpperCAmelCase       ,	**UpperCAmelCase      )

							lowerCamelCase_ 		=     vocab_size
							lowerCamelCase_ 		=     max_position_embeddings
							lowerCamelCase_ 		=     hidden_size
							lowerCamelCase_ 		=     num_hidden_layers
							lowerCamelCase_ 		=     num_attention_heads
							lowerCamelCase_ 		=     intermediate_size
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     hidden_dropout_prob
							lowerCamelCase_ 		=     attention_probs_dropout_prob
							lowerCamelCase_ 		=     initializer_range
							lowerCamelCase_ 		=     type_vocab_size
							lowerCamelCase_ 		=     layer_norm_eps
							lowerCamelCase_ 		=     position_embedding_type
							lowerCamelCase_ 		=     use_expectation
							lowerCamelCase_ 		=     hash_code_len
							lowerCamelCase_ 		=     num_hash
							lowerCamelCase_ 		=     conv_window
							lowerCamelCase_ 		=     use_fast_hash
							lowerCamelCase_ 		=     lsh_backward



