


"""simple docstring"""







import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer






class  __A							(			nn.Linear     ):






 """simple docstring"""




 def __init__(						self					,       __A					,       __A					,       __A = True					,       __A = "constant"					,       __A = 0.0					,       __A = "topK"					,       )   ->	Union[str, Any]:
     super(__A					,       self		).__init__(in_features=__A					,       out_features=__A					,       bias=__A		)
     assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
     a											=pruning_method

     if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
         a											=mask_scale
         a											=mask_init
         a											=nn.Parameter(torch.empty(self.weight.size()		)		)
         self.init_mask()


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	List[Any]:
     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  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	Any:
     # Get the mask
     if self.pruning_method == "topK":
         a											=TopKBinarizer.apply(self.mask_scores					,       __A		)
     elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
         a											='''sigmoied''' in self.pruning_method
         a											=ThresholdBinarizer.apply(self.mask_scores					,       __A					,       __A		)
     elif self.pruning_method == "magnitude":
         a											=MagnitudeBinarizer.apply(self.weight					,       __A		)
     elif self.pruning_method == "l0":
         a						, a						, a											=-0.1, 1.1, 2 / 3
         if self.training:
             a											=torch.zeros_like(self.mask_scores		).uniform_().clamp(0.0_001					,       0.9_999		)
             a											=torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b		)
         else:
             a											=torch.sigmoid(self.mask_scores		)
         a											=s * (r - l) + l
         a											=s_bar.clamp(min=0.0					,       max=1.0		)
     # Mask weights with computed mask
     a											=mask * self.weight
     # Compute output (linear layer) with masked weights
     return nn.functional.linear(__A					,       __A					,       self.bias		)



"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin






class  __A							(			unittest.TestCase,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 def  SCREAMING_SNAKE_CASE				(						self		)   ->	str:
     a											=load_tool('''document-question-answering'''		)
     self.tool.setup()
     a											=load_tool('''document-question-answering'''					,       remote=__A		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[Any]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     a											=self.tool(__A					,       '''When is the coffee break?'''		)
     self.assertEqual(__A					,       '''11-14 to 11:39 a.m.'''		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     a											=self.remote_tool(__A					,       '''When is the coffee break?'''		)
     self.assertEqual(__A					,       '''11-14 to 11:39 a.m.'''		)


 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Union[str, Any]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     self.tool(document=__A					,       question='''When is the coffee break?'''		)





 def  SCREAMING_SNAKE_CASE				(						self		)   ->	Optional[int]:
     a											=load_dataset('''hf-internal-testing/example-documents'''					,       split='''test'''		)
     a											=dataset[0]['''image''']

     a											=self.remote_tool(document=__A					,       question='''When is the coffee break?'''		)
     self.assertEqual(__A					,       '''11-14 to 11:39 a.m.'''		)