



'''simple docstring'''


import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase







@require_tokenizers
class 						UpperCAmelCase						(		unittest.TestCase):


 '''simple docstring'''




 __magic_name__       :			List[str]	=   EsmTokenizer




 def    lowercase_	( self)			->   Dict:



      """simple docstring"""







      super().setUp()
      a_									=tempfile.mkdtemp()
      # fmt: off
      a_									=["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
      # fmt: on
      a_									=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["vocab_file"])
      with open(self.vocab_file				,	"w"				,	encoding="utf-8") as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))




 def    lowercase_	( self				,	**lowerCAmelCase_)			->   List[PreTrainedTokenizerBase]:



      """simple docstring"""







      return [self.get_tokenizer(**lowerCAmelCase_)]




 def    lowercase_	( self				,	**lowerCAmelCase_)			->   PreTrainedTokenizer:



      """simple docstring"""







      return self.tokenizer_class.from_pretrained(self.tmpdirname				,	**lowerCAmelCase_)




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



      """simple docstring"""







      a_									=self.tokenizer_class(self.vocab_file)

      a_									=tokenizer.tokenize("LAGVS")
      self.assertListEqual(lowerCAmelCase_				,	["L", "A", "G", "V", "S"])
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_)				,	[4, 5, 6, 7, 8])




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



      """simple docstring"""







      a_									=self.tokenizer_class(self.vocab_file)

      a_									="LAGVS"
      self.assertListEqual(tokenizer.encode(lowerCAmelCase_)				,	[0, 4, 5, 6, 7, 8, 2])




 def    lowercase_	( self)			->   int:



      """simple docstring"""







      a_									=self.tokenizer_class(self.vocab_file)

      a_									=["LAGVS", "WCB"]
      a_									=tokenizer(lowerCAmelCase_				,	padding=lowerCAmelCase_)["input_ids"]

      self.assertListEqual(lowerCAmelCase_				,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]])




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



      """simple docstring"""







      a_									=self.tokenizer_class(self.vocab_file)

      a_									=["LAGVS", "WCB"]
      a_									=tokenizer(lowerCAmelCase_				,	padding=lowerCAmelCase_)["input_ids"]

      self.assertListEqual(lowerCAmelCase_				,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]])




 def    lowercase_	( self)			->   int:



      """simple docstring"""







      a_									=self.get_tokenizers(fast=lowerCAmelCase_)
      for tokenizer in tokenizers:
           with self.subTest(f"""{tokenizer.__class__.__name__}"""):
                a_									="<unk>"
                a_									="<mask>"

                a_									=tokenizer.tokenize(lowerCAmelCase_)
                a_									=tokenizer.tokenize(lowerCAmelCase_)

                self.assertEqual(len(lowerCAmelCase_)				,	1)
                self.assertEqual(len(lowerCAmelCase_)				,	1)
                self.assertEqual(token_a[0]				,	lowerCAmelCase_)
                self.assertEqual(token_a[0]				,	lowerCAmelCase_)







'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

     a_									=0  # Number of remaining (not pruned) params in the encoder
     a_									=0  # Number of params in the encoder

     print("name".ljust(6_0							,						" "							)							,						"Remaining Weights %"							,						"Remaining Weight"							)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    a_									=s * (r - l) + l
                    a_									=s_bar.clamp(min=0.0							,						max=1.0							)
                    a_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()

  parser.add_argument(
      '''--pruning_method''',
      choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
      type=str,
      required=True,
      help=(
          '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
          ''' pruning)'''
      ),
  )
  parser.add_argument(
      '''--threshold''',
      type=float,
      required=False,
      help=(
          '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
          '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
          '''Not needed for `l0`'''
      ),
  )
  parser.add_argument(
      '''--serialization_dir''',
      type=str,
      required=True,
      help='''Folder containing the model that was previously fine-pruned''',
  )

  lowercase											=      parser.parse_args()

  main(args)


