
'''simple docstring'''
import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


class        SCREAMING_SNAKE_CASE__	(			nn.Linear):







				def __init__(    self		,							A_		,							A_		,							A_ = True		,							A_ = "constant"		,							A_ = 0.0		,							A_ = "topK"		,							)->						Tuple:







											'''simple docstring'''






											super(A_		,							self			).__init__(in_features=A_		,							out_features=A_		,							bias=A_			)
											assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
											UpperCamelCase					       =			pruning_method

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







				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											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		,							A_		,							A_			)->						int:







											'''simple docstring'''






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







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types



class     lowercase						(       unittest.TestCase ,				_UpperCamelCase					):



 '''simple docstring'''


 def   UpperCamelCase__							(self ) ->					List[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				load_tool('translation' )
  self.tool.setup()
  UpperCAmelCase__							=				load_tool('translation'       ,       remote=__a )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool('Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				self.tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				self.remote_tool(text='Hey, what\'s up?'       ,       src_lang='English'       ,       tgt_lang='French' )
  self.assertEqual(__a       ,       '- Hé, comment ça va?' )




 def   UpperCamelCase__							(self ) ->					Any:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

  # There is a single output
  if len(self.tool.outputs ) == 1:
   UpperCAmelCase__							=				[outputs]

  self.assertListEqual(output_types(__a )       ,       self.tool.outputs )




 def   UpperCamelCase__							(self ) ->					Optional[int]:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']
  UpperCAmelCase__							=				self.tool(*__a )

  if not isinstance(__a       ,       __a ):
   UpperCAmelCase__							=				[outputs]

  self.assertEqual(len(__a )       ,       len(self.tool.outputs ) )

  for output, output_type in zip(__a       ,       self.tool.outputs ):
   UpperCAmelCase__							=				AGENT_TYPE_MAPPING[output_type]
   self.assertTrue(isinstance(__a       ,       __a ) )




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				['Hey, what\'s up?', 'English', 'Spanish']

  UpperCAmelCase__							=				[]

  for _input, input_type in zip(__a       ,       self.tool.inputs ):
   if isinstance(__a       ,       __a ):
    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
   else:
    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )

        # Should not raise an error
  UpperCAmelCase__							=				self.tool(*__a )

  if not isinstance(__a       ,       __a ):
   UpperCAmelCase__							=				[outputs]

  self.assertEqual(len(__a )       ,       len(self.tool.outputs ) )



