


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 						_A						(						_lowercase				)			->				List[Any]:


     """simple docstring"""


     __UpperCamelCase				=	args.serialization_dir
     __UpperCamelCase				=	args.pruning_method
     __UpperCamelCase				=	args.threshold

     __UpperCamelCase				=	torch.load(os.path.join(_lowercase			,       'pytorch_model.bin'				)			,       map_location='cpu'				)

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

     print('name'.ljust(60			,       ' '				)			,       '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":
                    __UpperCamelCase				=	TopKBinarizer.apply(_lowercase			,       _lowercase				).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __UpperCamelCase				=	ThresholdBinarizer.apply(_lowercase			,       _lowercase			,       _lowercase				).sum().item()
               elif pruning_method == "l0":
                    __UpperCamelCase,  __UpperCamelCase				=	-0.1, 1.1
                    __UpperCamelCase				=	torch.sigmoid(_lowercase				)
                    __UpperCamelCase				=	s * (r - l) + l
                    __UpperCamelCase				=	s_bar.clamp(min=0.0			,       max=1.0				)
                    __UpperCamelCase				=	(mask > 0.0).sum().item()
               else:
                    raise ValueError('Unknown pruning method'				)
               remaining_count += mask_ones
               print(name.ljust(60			,       ' '				)			,       str(round(1_00 * mask_ones / param.numel()			,       3				)				).ljust(20			,       ' '				)			,       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_00 * remaining_count / encoder_count				)


if __name__ == "__main__":
    __snake_case     =		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''',
    )

    __snake_case     =		parser.parse_args()

    main(args)






import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin



class 					__lowerCamelCase	(_a							,     unittest.TestCase		):
       _lowercase    							=      SpeechaTextaTokenizer
       _lowercase    							=      False







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            super().setUp()

            __UpperCamelCase				=	'<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(' '			)
            __UpperCamelCase				=	['he re</w> 123', 'here a 1456']
            __UpperCamelCase				=	dict(zip(A_,range(len(A_			)			)			)			)

            __UpperCamelCase				=	{'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

            __UpperCamelCase				=	tempfile.mkdtemp()
            __UpperCamelCase				=	os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['vocab_file']			)
            __UpperCamelCase				=	os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['merges_file']			)

            with open(self.vocab_file,'w',encoding='utf-8'			) as fp:
                 fp.write(json.dumps(A_			) + '\n'			)

            with open(self.merges_file,'w'			) as fp:
                 fp.write('\n'.join(A_			)			)







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




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

            self.assertEqual(vocab_keys[0],'<s>'			)
            self.assertEqual(vocab_keys[1],'<pad>'			)
            self.assertEqual(vocab_keys[-1],'vocab'			)
            self.assertEqual(len(A_			),14			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size,14			)







       def 						snake_case_					(	self:				Dict			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            # make sure @@ is correctly concatenated
            __UpperCamelCase				=	[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
            __UpperCamelCase				=	tokenizer.decode(A_			)

            self.assertTrue(output_string == 'herecouple words ofthe'			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 tokenizer.save_pretrained(A_			)
                 os.remove(os.path.join(A_,'merges.txt'			)			)

                 # load tokenizer without merges file should not throw an error
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            with tempfile.TemporaryDirectory() as tmp_dirname:
                 # save tokenizer and load again
                 tokenizer.save_pretrained(A_			)
                 __UpperCamelCase				=	SpeechaTextaTokenizer.from_pretrained(A_			)

            self.assertIsNotNone(A_			)







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




            if not self.test_slow_tokenizer:
                 return

            __UpperCamelCase				=	inspect.signature(self.tokenizer_class.__init__			)
            __UpperCamelCase				=	self.get_tokenizer()

            for parameter_name, parameter in signature.parameters.items():
                 if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                      self.assertIn(A_,tokenizer.init_kwargs			)


