'''simple docstring'''

import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import GPTaLMHeadModel







@require_torch
@require_sentencepiece
@require_tokenizers
class     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






 @slow
 @unittest.skip("""Model is not available."""							)
 def a (				self       :     str							)  ->   List[str]:
  __lowerCAmelCase    							=						"""nvidia/megatron-gpt2-345m/"""
  if "MYDIR" in os.environ:
   __lowerCAmelCase    							=						os.path.join(os.environ["""MYDIR"""]				,	SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
  model.to(SCREAMING_SNAKE_CASE__							)
  model.half()

  __lowerCAmelCase    							=						torch.tensor(
      [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]				,	device=SCREAMING_SNAKE_CASE__				,	dtype=torch.long				,	)

  with torch.no_grad():
   __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE__							).logits

  __lowerCAmelCase    							=						torch.Size((1, 9, 5_02_57)							)
  self.assertEqual(output.shape				,	SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						torch.tensor(
      [
          4.9_4_1_4,
          -0.2_9_2_0,
          -1.2_1_4_8,
          -4.0_2_7_3,
          -0.5_1_6_1,
          -5.2_1_0_9,
          -1.2_4_1_2,
          -1.8_3_0_1,
          -1.7_7_3_4,
          -4.7_1_4_8,
          -0.2_3_1_7,
          -1.0_8_1_1,
          -2.1_7_7_7,
          0.4_1_4_1,
          -3.7_9_6_9,
          -4.0_5_8_6,
          -2.5_3_3_2,
          -3.3_8_0_9,
          4.3_8_6_7,
      ]				,	device=SCREAMING_SNAKE_CASE__				,	dtype=torch.half				,	)

  for i in range(19							):
   __lowerCAmelCase							,	__lowerCAmelCase    							=						8 * i // 17, 27_92 * i  # along the diagonal
   __lowerCAmelCase							,	__lowerCAmelCase    							=						output[0, r, c], expected_diag[i]
   __lowerCAmelCase    							=						f"""row={r} col={c} computed={computed} expected={expected}"""
   self.assertAlmostEqual(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	delta=1e-4				,	msg=SCREAMING_SNAKE_CASE__							)







'''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							)






