




import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      BarthezTokenizer
    UpperCAmelCase__							  =      BarthezTokenizerFast
    UpperCAmelCase__							  =      True
    UpperCAmelCase__							  =      True


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Dict:





           '''simple docstring'''

           super().setUp()

           A__       =    BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''')
           tokenizer.save_pretrained(self.tmpdirname)
           tokenizer.save_pretrained(self.tmpdirname					, legacy_format=UpperCAmelCase__)
           A__       =    tokenizer


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Union[str, Any]:





           '''simple docstring'''

           A__       =    '''<pad>'''
           A__       =    1

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Tuple:





           '''simple docstring'''

           A__       =    list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0]					, '''<s>''')
           self.assertEqual(vocab_keys[1]					, '''<pad>''')
           self.assertEqual(vocab_keys[-1]					, '''<mask>''')
           self.assertEqual(len(UpperCAmelCase__)					, 101_122)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Any:





           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size					, 101_122)


    @require_torch
    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->str:





           '''simple docstring'''

           A__       =    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           A__       =    [0, 57, 3_018, 70_307, 91, 2]

           A__       =    self.tokenizer(
               UpperCAmelCase__					, max_length=len(UpperCAmelCase__)					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__					, return_tensors='''pt''')
           self.assertIsInstance(UpperCAmelCase__					, UpperCAmelCase__)

           self.assertEqual((2, 6)					, batch.input_ids.shape)
           self.assertEqual((2, 6)					, batch.attention_mask.shape)
           A__       =    batch.input_ids.tolist()[0]
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Optional[Any]:





           '''simple docstring'''

           if not self.test_rust_tokenizer:
                  return

           A__       =    self.get_tokenizer()
           A__       =    self.get_rust_tokenizer()

           A__       =    '''I was born in 92000, and this is falsé.'''

           A__       =    tokenizer.tokenize(UpperCAmelCase__)
           A__       =    rust_tokenizer.tokenize(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)

           A__       =    tokenizer.encode(UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__)
           A__       =    rust_tokenizer.encode(UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)

           A__       =    self.get_rust_tokenizer()
           A__       =    tokenizer.encode(UpperCAmelCase__)
           A__       =    rust_tokenizer.encode(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__					, UpperCAmelCase__)





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           A__       =    {'''input_ids''': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 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], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], '''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, 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, 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]]}  # noqa: E501
           # fmt: on

           # moussaKam/mbarthez is a french model. So we also use french texts.
           A__       =    [
               '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
               '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
               '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
               '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
               '''telles que la traduction et la synthèse de texte.''',
           ]

           self.tokenizer_integration_test_util(
               expected_encoding=UpperCAmelCase__					, model_name='''moussaKam/mbarthez'''					, revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''					, sequences=UpperCAmelCase__					, )








import argparse
import os
import shutil

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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       A__       =    args.pruning_method
       A__       =    args.threshold

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

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

       for name, tensor in model.items():
              if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "classifier" in name or "qa_output" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "bias" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              else:
                     if pruning_method == "magnitude":
                            A__       =    MagnitudeBinarizer.apply(inputs=lowercase_ ,				threshold=lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "topK":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    TopKBinarizer.apply(lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "sigmoied_threshold":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    ThresholdBinarizer.apply(lowercase_ ,				lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "l0":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            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__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     else:
                            raise ValueError('''Unknown pruning method'''						)

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

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

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


if __name__ == "__main__":
  _lowerCamelCase	:		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""",
  )

  _lowerCamelCase	:		int							       =   parser.parse_args()

  main(args)


