
"""simple docstring"""
import argparse
import os
import shutil

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

def       lowercase			(_lowerCAmelCase		):
						__lowerCAmelCase   					=   args.pruning_method
						__lowerCAmelCase   					=   args.threshold

						__lowerCAmelCase   					=   args.model_name_or_path.rstrip("""/"""		)
						__lowerCAmelCase   					=   args.target_model_path

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

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

						if target_model_path is None:
												__lowerCAmelCase   					=   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__":
			SCREAMING_SNAKE_CASE_ 			=     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''',
			)

			SCREAMING_SNAKE_CASE_ 			=     parser.parse_args()

			main(args)






import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






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





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


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





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


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





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





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





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


