

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
      from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
      a_     :    Optional[Any]     		=   get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
      from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

a_     :    List[str]     		=   12_8022
a_     :    Dict     		=   12_8028




@require_sentencepiece
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE						,      unittest.TestCase ):
    __UpperCamelCase    =MaMaaaTokenizer
    __UpperCamelCase    =False
    __UpperCamelCase    =False
    __UpperCamelCase    =True

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     super().setUp()

     SCREAMING_SNAKE_CASE		    =      ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
     SCREAMING_SNAKE_CASE		    =      dict(zip(snake_case__				,     range(len(snake_case__     )     )     )     )
     SCREAMING_SNAKE_CASE		    =      Path(self.tmpdirname     )
     save_json(snake_case__				,     save_dir / VOCAB_FILES_NAMES['vocab_file']     )
     if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
      copyfile(snake_case__				,     save_dir / VOCAB_FILES_NAMES['spm_file']     )

     SCREAMING_SNAKE_CASE		    =      MaMaaaTokenizer.from_pretrained(self.tmpdirname     )
     tokenizer.save_pretrained(self.tmpdirname     )

    def UpperCamelCase      (      self			:   Optional[Any]				,     **snake_case__			:   Tuple     ):

     """simple docstring"""






     return MaMaaaTokenizer.from_pretrained(self.tmpdirname				,     **snake_case__     )

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   List[str]     ):

     """simple docstring"""






     return (
         "This is a test",
         "This is a test",
     )

    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      '</s>'
     SCREAMING_SNAKE_CASE		    =      0

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

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_tokenizer()
     SCREAMING_SNAKE_CASE		    =      list(tokenizer.get_vocab().keys()     )

     self.assertEqual(vocab_keys[0]				,     '</s>'     )
     self.assertEqual(vocab_keys[1]				,     '<unk>'     )
     self.assertEqual(vocab_keys[-1]				,     '<s>'     )
     self.assertEqual(len(snake_case__     )				,     tokenizer.vocab_size + len(tokenizer.get_added_vocab()     )     )

    @unittest.skip('Skip this test while all models are still to be uploaded.'     )
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     pass

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_tokenizer()

     SCREAMING_SNAKE_CASE		    =      tokenizer.tokenize('This is a test'     )
     self.assertListEqual(snake_case__				,     ['▁This', '▁is', '▁a', '▁t', 'est']     )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(snake_case__     )				,     [2, 3, 4, 5, 6]				,     )

     SCREAMING_SNAKE_CASE		    =      tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]     )
     self.assertListEqual(snake_case__				,     ['▁This', '▁is', '▁a', '▁t', 'est']     )

     SCREAMING_SNAKE_CASE		    =      tokenizer.convert_tokens_to_string(snake_case__     )
     self.assertEqual(snake_case__				,     'This is a test'     )




    @slow
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      {'input_ids': [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 2, 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, 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_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1]], '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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=snake_case__				,     model_name='facebook/m2m100_418M'				,     revision='c168bae485c864188cf9aa0e4108b0b6934dc91e'				,     )




@require_torch
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase			(  unittest.TestCase ):
    __UpperCamelCase    ="facebook/m2m100_418M"
    __UpperCamelCase    =[
        "In my opinion, there are two levels of response from the French government.",
        "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
    ]
    __UpperCamelCase    =[
        "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
        "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
    ]

    # fmt: off
    __UpperCamelCase    =[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]

    @classmethod
    def UpperCamelCase      (      cls			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      MaMaaaTokenizer.from_pretrained(
         cls.checkpoint_name				,     src_lang='en'				,     tgt_lang='fr'     )
     SCREAMING_SNAKE_CASE		    =      1
     return cls

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     self.assertEqual(self.tokenizer.get_lang_id('ar'     )				,     1_2_8_0_0_6     )
     self.assertEqual(self.tokenizer.get_lang_id('en'     )				,     1_2_8_0_2_2     )
     self.assertEqual(self.tokenizer.get_lang_id('ro'     )				,     1_2_8_0_7_6     )
     self.assertEqual(self.tokenizer.get_lang_id('mr'     )				,     1_2_8_0_6_3     )

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer.get_vocab()
     self.assertEqual(len(snake_case__     )				,     self.tokenizer.vocab_size     )
     self.assertEqual(vocab['<unk>']				,     3     )
     self.assertIn(self.tokenizer.get_lang_token('en'     )				,     snake_case__     )

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'en'
     SCREAMING_SNAKE_CASE		    =      self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
     self.assertListEqual(self.expected_src_tokens				,     snake_case__     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     self.assertIn(snake_case__				,     self.tokenizer.all_special_ids     )
     # fmt: off
     SCREAMING_SNAKE_CASE		    =      [FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
     # fmt: on
     SCREAMING_SNAKE_CASE		    =      self.tokenizer.decode(snake_case__				,     skip_special_tokens=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      self.tokenizer.decode(generated_ids[1:]				,     skip_special_tokens=snake_case__     )
     self.assertEqual(snake_case__				,     snake_case__     )
     self.assertNotIn(self.tokenizer.eos_token				,     snake_case__     )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE		    =      self.tokenizer.lang_token_to_id
     self.tokenizer.save_pretrained(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      MaMaaaTokenizer.from_pretrained(snake_case__     )
     self.assertDictEqual(new_tok.lang_token_to_id				,     snake_case__     )

    @require_torch
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'en'
     SCREAMING_SNAKE_CASE		    =      'fr'

     SCREAMING_SNAKE_CASE		    =      self.tokenizer(self.src_text				,     text_target=self.tgt_text				,     padding=snake_case__				,     return_tensors='pt'     )

     SCREAMING_SNAKE_CASE		    =      shift_tokens_right(
         batch['labels']				,     self.tokenizer.pad_token_id				,     self.tokenizer.eos_token_id     )

     for k in batch:
      SCREAMING_SNAKE_CASE		    =      batch[k].tolist()
     # batch = {k: v.tolist() for k,v in batch.items()}
     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     # batch.decoder_inputs_ids[0][0] ==
     assert batch.input_ids[1][0] == EN_CODE
     assert batch.input_ids[1][-1] == 2
     assert batch.labels[1][0] == FR_CODE
     assert batch.labels[1][-1] == 2
     assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]

    @require_torch
    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'mr'
     self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id('mr'     )]     )
     self.assertListEqual(self.tokenizer.suffix_tokens				,     [self.tokenizer.eos_token_id]     )

     SCREAMING_SNAKE_CASE		    =      'zh'
     self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id('zh'     )]     )
     self.assertListEqual(self.tokenizer.suffix_tokens				,     [self.tokenizer.eos_token_id]     )

    @require_torch
    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      'mr'
     self.tokenizer._switch_to_target_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id('mr'     )]     )
     self.assertListEqual(self.tokenizer.suffix_tokens				,     [self.tokenizer.eos_token_id]     )
     self.tokenizer._switch_to_input_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )

     SCREAMING_SNAKE_CASE		    =      'zh'
     self.tokenizer._switch_to_target_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id('zh'     )]     )
     self.assertListEqual(self.tokenizer.suffix_tokens				,     [self.tokenizer.eos_token_id]     )
     self.tokenizer._switch_to_input_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens				,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )




    @require_torch
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer._build_translation_inputs('A test'				,     return_tensors='pt'				,     src_lang='en'				,     tgt_lang='ar'     )

     self.assertEqual(
         nested_simplify(snake_case__     )				,     {
             # en_XX, A, test, EOS
             'input_ids': [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
             'attention_mask': [[1, 1, 1, 1]],
             # ar_AR
             'forced_bos_token_id': 1_2_8_0_0_6,
         }				,     )




import argparse
import os
import shutil

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      args.pruning_method
 SCREAMING_SNAKE_CASE		    =      args.threshold

 SCREAMING_SNAKE_CASE		    =      args.model_name_or_path.rstrip('/'						)
 SCREAMING_SNAKE_CASE		    =      args.target_model_path

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

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

 if target_model_path is None:
  SCREAMING_SNAKE_CASE		    =      os.path.join(
      os.path.dirname(_UpperCamelCase						)		,				f"""bertarized_{os.path.basename(_UpperCamelCase						)}"""						)

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

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


if __name__ == "__main__":
      a_     :    Union[str, 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",
      )

      a_     :    Dict     		=   parser.parse_args()

      main(args)

