





'''simple docstring'''



import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				args.pruning_method
  _snake_case     		=				args.threshold

  _snake_case     		=				args.model_name_or_path.rstrip("/"       )
  _snake_case     		=				args.target_model_path

  print(f'''Load fine-pruned model from {model_name_or_path}'''       )
  _snake_case     		=				torch.load(os.path.join(__lowerCAmelCase					,	"pytorch_model.bin"       )       )
  _snake_case     		=				{}

  for name, tensor in model.items():
    if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
      _snake_case     		=				tensor
      print(f'''Copied layer {name}'''       )
    elif "classifier" in name or "qa_output" in name:
      _snake_case     		=				tensor
      print(f'''Copied layer {name}'''       )
    elif "bias" in name:
      _snake_case     		=				tensor
      print(f'''Copied layer {name}'''       )
    else:
      if pruning_method == "magnitude":
        _snake_case     		=				MagnitudeBinarizer.apply(inputs=__lowerCAmelCase					,	threshold=__lowerCAmelCase       )
        _snake_case     		=				tensor * mask
        print(f'''Pruned layer {name}'''       )
      elif pruning_method == "topK":
        if "mask_scores" in name:
          continue
        _snake_case     		=				name[:-6]
        _snake_case     		=				model[f'''{prefix_}mask_scores''']
        _snake_case     		=				TopKBinarizer.apply(__lowerCAmelCase					,	__lowerCAmelCase       )
        _snake_case     		=				tensor * mask
        print(f'''Pruned layer {name}'''       )
      elif pruning_method == "sigmoied_threshold":
        if "mask_scores" in name:
          continue
        _snake_case     		=				name[:-6]
        _snake_case     		=				model[f'''{prefix_}mask_scores''']
        _snake_case     		=				ThresholdBinarizer.apply(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase       )
        _snake_case     		=				tensor * mask
        print(f'''Pruned layer {name}'''       )
      elif pruning_method == "l0":
        if "mask_scores" in name:
          continue
        _snake_case     		=				name[:-6]
        _snake_case     		=				model[f'''{prefix_}mask_scores''']
        _snake_case     		=				-0.1, 1.1
        _snake_case     		=				torch.sigmoid(__lowerCAmelCase       )
        _snake_case     		=				s * (r - l) + l
        _snake_case     		=				s_bar.clamp(min=0.0					,	max=1.0       )
        _snake_case     		=				tensor * mask
        print(f'''Pruned layer {name}'''       )
      else:
        raise ValueError("Unknown pruning method"       )

  if target_model_path is None:
    _snake_case     		=				os.path.join(
        os.path.dirname(__lowerCAmelCase       )					,	f'''bertarized_{os.path.basename(__lowerCAmelCase       )}'''       )

  if not os.path.isdir(__lowerCAmelCase       ):
    shutil.copytree(__lowerCAmelCase					,	__lowerCAmelCase       )
    print(f'''\nCreated folder {target_model_path}'''       )

  torch.save(__lowerCAmelCase					,	os.path.join(__lowerCAmelCase					,	"pytorch_model.bin"       )       )
  print("\nPruned model saved! See you later!"       )


if __name__ == "__main__":
 __magic_name__   :						Optional[Any]    =			argparse.ArgumentParser()

 parser.add_argument(
     """--pruning_method""",
     choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
     type=str,
     required=True,
     help=(
         """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
         """ sigmoied_threshold = Soft movement pruning)"""
     ),
 )
 parser.add_argument(
     """--threshold""",
     type=float,
     required=False,
     help=(
         """For `magnitude` and `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(
     """--model_name_or_path""",
     type=str,
     required=True,
     help="""Folder containing the model that was previously fine-pruned""",
 )
 parser.add_argument(
     """--target_model_path""",
     default=None,
     type=str,
     required=False,
     help="""Folder containing the model that was previously fine-pruned""",
 )

 __magic_name__   :						Union[str, Any]    =			parser.parse_args()

 main(args)







'''simple docstring'''



import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__   :						Optional[Any]    =			get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Any      =   ReformerTokenizer
     UpperCAmelCase__       :						List[str]      =   ReformerTokenizerFast
     UpperCAmelCase__       :						Any      =   True
     UpperCAmelCase__       :						Union[str, Any]      =   False
     UpperCAmelCase__       :						Optional[Any]      =   True

     def 	UpperCamelCase(					self	):
       super().setUp()

       _snake_case     		=				ReformerTokenizer(lowerCamelCase						,   keep_accents=lowerCamelCase	)
       tokenizer.save_pretrained(self.tmpdirname	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				"<s>"
       _snake_case     		=				1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase	)						,   lowerCamelCase	)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase	)						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				list(self.get_tokenizer().get_vocab().keys()	)

       self.assertEqual(vocab_keys[0]						,   "<unk>"	)
       self.assertEqual(vocab_keys[1]						,   "<s>"	)
       self.assertEqual(vocab_keys[-1]						,   "j"	)
       self.assertEqual(len(lowerCamelCase	)						,   1_000	)

     def 	UpperCamelCase(					self	):
       self.assertEqual(self.get_tokenizer().vocab_size						,   1_000	)

     def 	UpperCamelCase(					self	):
       if not self.test_rust_tokenizer:
         return

       _snake_case     		=				self.get_tokenizer()
       _snake_case     		=				self.get_rust_tokenizer()

       _snake_case     		=				"I was born in 92000, and this is falsé."

       _snake_case     		=				tokenizer.tokenize(lowerCamelCase	)
       _snake_case     		=				rust_tokenizer.tokenize(lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
       _snake_case     		=				rust_tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

       _snake_case     		=				self.get_rust_tokenizer()
       _snake_case     		=				tokenizer.encode(lowerCamelCase	)
       _snake_case     		=				rust_tokenizer.encode(lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase=15	):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
           _snake_case     		=				self.rust_tokenizer_class.from_pretrained(lowerCamelCase						,   **lowerCamelCase	)

           # Simple input
           _snake_case     		=				"This is a simple input"
           _snake_case     		=				["This is a simple input 1", "This is a simple input 2"]
           _snake_case     		=				("This is a simple input", "This is a pair")
           _snake_case     		=				[
               ("This is a simple input 1", "This is a simple input 2"),
               ("This is a simple pair 1", "This is a simple pair 2"),
           ]

           # Simple input tests
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Simple input
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Simple input
           self.assertRaises(
               lowerCamelCase						,   tokenizer_r.batch_encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"						,   )

           # Pair input
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Pair input
           self.assertRaises(lowerCamelCase						,   tokenizer_r.encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"	)

           # Pair input
           self.assertRaises(
               lowerCamelCase						,   tokenizer_r.batch_encode_plus						,   lowerCamelCase						,   max_length=lowerCamelCase						,   padding="max_length"						,   )

     def 	UpperCamelCase(					self	):
       pass

     def 	UpperCamelCase(					self	):
       _snake_case     		=				ReformerTokenizer(lowerCamelCase						,   keep_accents=lowerCamelCase	)

       _snake_case     		=				tokenizer.tokenize("This is a test"	)
       self.assertListEqual(lowerCamelCase						,   ["▁This", "▁is", "▁a", "▁t", "est"]	)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   [285, 46, 10, 170, 382]						,   )

       _snake_case     		=				tokenizer.tokenize("I was born in 92000, and this is falsé."	)
       self.assertListEqual(
           lowerCamelCase						,   [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "9",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "é",
               ".",
           ]						,   )
       _snake_case     		=				tokenizer.convert_tokens_to_ids(lowerCamelCase	)
       self.assertListEqual(
           lowerCamelCase						,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,   )

       _snake_case     		=				tokenizer.convert_ids_to_tokens(lowerCamelCase	)
       self.assertListEqual(
           lowerCamelCase						,   [
               SPIECE_UNDERLINE + "I",
               SPIECE_UNDERLINE + "was",
               SPIECE_UNDERLINE + "b",
               "or",
               "n",
               SPIECE_UNDERLINE + "in",
               SPIECE_UNDERLINE + "",
               "<unk>",
               "2",
               "0",
               "0",
               "0",
               ",",
               SPIECE_UNDERLINE + "and",
               SPIECE_UNDERLINE + "this",
               SPIECE_UNDERLINE + "is",
               SPIECE_UNDERLINE + "f",
               "al",
               "s",
               "<unk>",
               ".",
           ]						,   )

     @cached_property
     def 	UpperCamelCase(					self	):
       return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"	)

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				"Hello World!"
       _snake_case     		=				[126, 32, 262, 152, 38, 72, 287]

       self.assertListEqual(lowerCamelCase						,   self.big_tokenizer.encode(lowerCamelCase	)	)

     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				(
           "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
           " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
       )
       _snake_case     		=				[
           108,
           265,
           24,
           111,
           4,
           258,
           156,
           35,
           28,
           275,
           3,
           259,
           297,
           260,
           84,
           4,
           35,
           110,
           44,
           8,
           259,
           91,
           268,
           21,
           11,
           209,
           274,
           109,
           266,
           277,
           117,
           86,
           93,
           315,
           258,
           278,
           258,
           277,
           258,
           0,
           258,
           288,
           258,
           319,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           287,
           258,
           315,
           258,
           289,
           258,
           278,
           99,
           269,
           266,
           262,
           8,
           259,
           241,
           4,
           217,
           230,
           268,
           266,
           55,
           168,
           106,
           75,
           193,
           266,
           223,
           27,
           49,
           26,
           282,
           25,
           264,
           299,
           19,
           26,
           0,
           258,
           277,
           117,
           86,
           93,
           176,
           183,
           270,
           11,
           262,
           42,
           61,
           265,
       ]

       self.assertListEqual(lowerCamelCase						,   self.big_tokenizer.encode(lowerCamelCase	)	)

     @require_torch
     @slow
     def 	UpperCamelCase(					self	):
       import torch

       from transformers import ReformerConfig, ReformerModel

       # Build sequence
       _snake_case     		=				list(self.big_tokenizer.get_vocab().keys()	)[:10]
       _snake_case     		=				" ".join(lowerCamelCase	)
       _snake_case     		=				self.big_tokenizer.encode_plus(lowerCamelCase						,   return_tensors="pt"	)
       _snake_case     		=				self.big_tokenizer.batch_encode_plus([sequence, sequence]						,   return_tensors="pt"	)

       _snake_case     		=				ReformerConfig()
       # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
       _snake_case     		=				encoded_sequence["input_ids"].shape
       _snake_case     		=				ReformerModel(lowerCamelCase	)

       # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
         model(**lowerCamelCase	)
         model(**lowerCamelCase	)






     @slow
     def 	UpperCamelCase(					self	):
       # fmt: off
       _snake_case     		=				{"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # This tokenizer does not know some characters like ")".
       # That is the reason why we use very simple texts here.
       # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
       _snake_case     		=				[
           "This is a very simple sentence.",
           "The quick brown fox jumps over the lazy dog.",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=lowerCamelCase						,   model_name="google/reformer-crime-and-punishment"						,   revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"						,   padding=lowerCamelCase						,   sequences=lowerCamelCase						,   )


