
import argparse
import os
import shutil

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


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			Optional[int]	    =       args.pruning_method
    __lowercase		:			List[Any]	    =       args.threshold

    __lowercase		:			str	    =       args.model_name_or_path.rstrip('''/'''	)
    __lowercase		:			Union[str, Any]	    =       args.target_model_path

    print(F'Load fine-pruned model from {model_name_or_path}'	)
    __lowercase		:			List[str]	    =       torch.load(os.path.join(__lowerCAmelCase	,					'''pytorch_model.bin'''	)	)
    __lowercase		:			Optional[Any]	    =       {}

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

    if target_model_path is None:
        __lowercase		:			Any	    =       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__":
 __lowerCAmelCase					:			Optional[int]			      =		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					:			Optional[Any]			      =		parser.parse_args()

 main(args)


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 						__lowerCAmelCase	(	lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			Dict        =    None
      A__	:			Any        =    BloomTokenizerFast
      A__	:			Optional[Any]        =    BloomTokenizerFast
      A__	:			List[Any]        =    True
      A__	:			int        =    False
      A__	:			Any        =    '''tokenizer_file'''
      A__	:			Optional[int]        =    {'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




      def 		snake_case_			(      self		:       Any				):
          super().setUp()
          __lowercase		:			int	    =       BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''				)
          tokenizer.save_pretrained(self.tmpdirname				)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       Any				):
          kwargs.update(self.special_tokens_map				)
          return BloomTokenizerFast.from_pretrained(self.tmpdirname				,       **_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Dict	    =       self.get_rust_tokenizer()

          __lowercase		:			Optional[int]	    =       ['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
          __lowercase		:			Union[str, Any]	    =       [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

          __lowercase		:			Optional[int]	    =       tokenizer.batch_encode_plus(_snake_case				)['''input_ids''']
          self.assertListEqual(_snake_case				,       _snake_case				)

          __lowercase		:			List[Any]	    =       tokenizer.batch_decode(_snake_case				)
          self.assertListEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       str				,       _snake_case		:       int=6				):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                  __lowercase		:			Dict	    =       self.rust_tokenizer_class.from_pretrained(_snake_case				,       **_snake_case				)
                  # tokenizer_r.pad_token = None # Hotfixing padding = None
                  # Simple input
                  __lowercase		:			List[Any]	    =       '''This is a simple input'''
                  __lowercase		:			List[str]	    =       ['''This is a simple input 1''', '''This is a simple input 2''']
                  __lowercase		:			Optional[Any]	    =       ('''This is a simple input''', '''This is a pair''')
                  __lowercase		:			Any	    =       [
                      ('''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
                  try:
                      tokenizer_r.encode(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.encode_plus(_snake_case				,       max_length=_snake_case				)

                      tokenizer_r.batch_encode_plus(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.encode(_snake_case				,       max_length=_snake_case				)
                      tokenizer_r.batch_encode_plus(_snake_case				,       max_length=_snake_case				)
                  except ValueError:
                      self.fail('''Bloom Tokenizer should be able to deal with padding'''				)

                  __lowercase		:			Dict	    =       None  # Hotfixing padding = None
                  self.assertRaises(_snake_case				,       tokenizer_r.encode				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Simple input
                  self.assertRaises(_snake_case				,       tokenizer_r.encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Simple input
                  self.assertRaises(
                      _snake_case				,       tokenizer_r.batch_encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				,       )

                  # Pair input
                  self.assertRaises(_snake_case				,       tokenizer_r.encode				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Pair input
                  self.assertRaises(_snake_case				,       tokenizer_r.encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				)

                  # Pair input
                  self.assertRaises(
                      _snake_case				,       tokenizer_r.batch_encode_plus				,       _snake_case				,       max_length=_snake_case				,       padding='''max_length'''				,       )




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			List[Any]	    =       self.get_rust_tokenizer()
          __lowercase		:			int	    =       load_dataset('''xnli'''				,       '''all_languages'''				,       split='''test'''				,       streaming=_snake_case				)

          __lowercase		:			Optional[Any]	    =       next(iter(_snake_case				)				)['''premise''']  # pick up one data
          __lowercase		:			List[str]	    =       list(sample_data.values()				)

          __lowercase		:			List[str]	    =       list(map(tokenizer.encode				,       _snake_case				)				)
          __lowercase		:			Tuple	    =       [tokenizer.decode(_snake_case				,       clean_up_tokenization_spaces=_snake_case				) for x in output_tokens]
          self.assertListEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
          # any sequence length constraints. This test of the parent class will fail since it relies on the
          # maximum sequence length of the positoonal embeddings.
          self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)				,       1				)
          self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)				,       1				)


