
"""simple docstring"""




import itertools
import string
from collections.abc import Generator, Iterable


def   __magic_name__	(      __snake_case				:       Iterable[str]     , __snake_case				:       int  ) ->				Generator[tuple[str, ...], None, None]:
       lowercase    :				Dict       		=							iter(__lowercase  )
       while True:
              lowercase    :				str       		=							tuple(itertools.islice(__lowercase     , __lowercase  )  )
              if not chunk:
                     return
              yield chunk


def   __magic_name__	(      __snake_case				:       str  ) ->				str:
       lowercase    :				str       		=							"".join([c.upper() for c in dirty if c in string.ascii_letters]  )
       lowercase    :				int       		=							""

       if len(__lowercase  ) < 2:
              return dirty

       for i in range(len(__lowercase  ) - 1  ):
              clean += dirty[i]

              if dirty[i] == dirty[i + 1]:
                     clean += "X"

       clean += dirty[-1]

       if len(__lowercase  ) & 1:
              clean += "X"

       return clean


def   __magic_name__	(      __snake_case				:       str  ) ->				list[str]:
       # I and J are used interchangeably to allow
       # us to use a 5x5 table (25 letters)
       lowercase    :				Dict       		=							"ABCDEFGHIKLMNOPQRSTUVWXYZ"
       # we're using a list instead of a '2d' array because it makes the math
       # for setting up the table and doing the actual encoding/decoding simpler
       lowercase    :				Tuple       		=							[]

       # copy key chars into the table if they are in `alphabet` ignoring duplicates
       for char in key.upper():
              if char not in table and char in alphabet:
                     table.append(__lowercase  )

    # fill the rest of the table in with the remaining alphabet chars
       for char in alphabet:
              if char not in table:
                     table.append(__lowercase  )

       return table


def   __magic_name__	(      __snake_case				:       str     , __snake_case				:       str  ) ->				str:
       lowercase    :				int       		=							generate_table(__lowercase  )
       lowercase    :				int       		=							prepare_input(__lowercase  )
       lowercase    :				Optional[int]       		=							""

       # https://en.wikipedia.org/wiki/Playfair_cipher#Description
       for chara, chara in chunker(__lowercase     , 2  ):
              lowercase		,    lowercase    :				str       		=							divmod(table.index(__lowercase  )     , 5  )
              lowercase		,    lowercase    :				Optional[int]       		=							divmod(table.index(__lowercase  )     , 5  )

              if rowa == rowa:
                     ciphertext += table[rowa * 5 + (cola + 1) % 5]
                     ciphertext += table[rowa * 5 + (cola + 1) % 5]
              elif cola == cola:
                     ciphertext += table[((rowa + 1) % 5) * 5 + cola]
                     ciphertext += table[((rowa + 1) % 5) * 5 + cola]
              else:  # rectangle
                     ciphertext += table[rowa * 5 + cola]
                     ciphertext += table[rowa * 5 + cola]

       return ciphertext


def   __magic_name__	(      __snake_case				:       str     , __snake_case				:       str  ) ->				str:
       lowercase    :				Dict       		=							generate_table(__lowercase  )
       lowercase    :				Any       		=							""

       # https://en.wikipedia.org/wiki/Playfair_cipher#Description
       for chara, chara in chunker(__lowercase     , 2  ):
              lowercase		,    lowercase    :				str       		=							divmod(table.index(__lowercase  )     , 5  )
              lowercase		,    lowercase    :				int       		=							divmod(table.index(__lowercase  )     , 5  )

              if rowa == rowa:
                     plaintext += table[rowa * 5 + (cola - 1) % 5]
                     plaintext += table[rowa * 5 + (cola - 1) % 5]
              elif cola == cola:
                     plaintext += table[((rowa - 1) % 5) * 5 + cola]
                     plaintext += table[((rowa - 1) % 5) * 5 + cola]
              else:  # rectangle
                     plaintext += table[rowa * 5 + cola]
                     plaintext += table[rowa * 5 + cola]

       return plaintext




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)