



'''simple docstring'''





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCAmelCase      						=	50_0000

_lowerCAmelCase						,			_lowerCAmelCase      						=	os.path.split(__file__)
_lowerCAmelCase      						=	os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    **UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					dataset.map(**UpperCamelCase			)
@get_duration
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    **UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[Any]							      =					dataset.filter(**UpperCamelCase			)
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					{"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        lowerCAmelCase__ :  List[str]							      =					datasets.Features({"""text""": datasets.Value("""string"""			), """numbers""": datasets.Value("""float32"""			)}			)
        lowerCAmelCase__ :  Union[str, Any]							      =					generate_example_dataset(
            os.path.join(UpperCamelCase							,    """dataset.arrow"""			)							,    UpperCamelCase							,    num_examples=UpperCamelCase			)

        lowerCAmelCase__ :  Optional[Any]							      =					transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""							,    use_fast=UpperCamelCase			)

        def tokenize(UpperCamelCase			):
            return tokenizer(examples["""text"""]			)

        lowerCAmelCase__ :  int							      =					map(UpperCamelCase			)

        lowerCAmelCase__ :  List[Any]							      =					map(UpperCamelCase							,    batched=UpperCamelCase			)

        lowerCAmelCase__ :  int							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""numpy"""			):
            lowerCAmelCase__ :  Optional[Any]							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""pandas"""			):
            lowerCAmelCase__ :  Dict							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""torch"""							,    columns="""numbers"""			):
            lowerCAmelCase__ :  Any							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        with dataset.formatted_as(type="""tensorflow"""							,    columns="""numbers"""			):
            lowerCAmelCase__ :  Dict							      =					map(UpperCamelCase							,    function=lambda UpperCamelCase			: None							,    batched=UpperCamelCase			)

        lowerCAmelCase__ :  Optional[int]							      =					map(UpperCamelCase							,    function=UpperCamelCase							,    batched=UpperCamelCase			)

        lowerCAmelCase__ :  Any							      =					filter(UpperCamelCase			)

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(UpperCamelCase							,    """wb"""			) as f:
        f.write(json.dumps(UpperCamelCase			).encode("""utf-8"""			)			)


if __name__ == "__main__":  # useful to run the profiler
 benchmark_map_filter()




import argparse
import os
import shutil

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






def        a_		(       __lowercase   :	Dict ) ->  List[Any]:
			_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(__lowercase       ,					'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=__lowercase       ,					threshold=__lowercase )
												_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(__lowercase       ,					__lowercase )
												_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(__lowercase       ,					__lowercase       ,					__lowercase )
												_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	,					_snake_case		=			-0.1, 1.1
												_snake_case		=			torch.sigmoid(__lowercase )
												_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(__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       :				Dict       =			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)