




'''simple docstring'''


def 	_a       (				__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  []
		snake_case__						:       Tuple       						=  []
		snake_case__						:       Union[str, Any]       						=  {
		    '^': 3,
		    '*': 2,
		    '/': 2,
		    '%': 2,
		    '+': 1,
		    '-': 1,
		}  # Priority of each operator
		snake_case__						:       List[str]       						=  len(__lowerCAmelCase     ) if (len(__lowerCAmelCase     ) > 7) else 7

		# Print table header for output
		print(
		    '''Symbol'''.center(8     )   ,							'''Stack'''.center(__lowerCAmelCase     )   ,							'''Postfix'''.center(__lowerCAmelCase     )   ,							sep=''' | '''   ,							)
		print('''-''' * (print_width * 3 + 7)     )

		for x in infix:
				if x.isalpha() or x.isdigit():
						post_fix.append(__lowerCAmelCase     )  # if x is Alphabet / Digit, add it to Postfix
				elif x == "(":
						stack.append(__lowerCAmelCase     )  # if x is "(" push to Stack
				elif x == ")":  # if x is ")" pop stack until "(" is encountered
						while stack[-1] != "(":
								post_fix.append(stack.pop()     )  # Pop stack & add the content to Postfix
						stack.pop()
				else:
						if len(__lowerCAmelCase     ) == 0:
								stack.append(__lowerCAmelCase     )  # If stack is empty, push x to stack
						else:  # while priority of x is not > priority of element in the stack
								while len(__lowerCAmelCase     ) > 0 and priority[x] <= priority[stack[-1]]:
										post_fix.append(stack.pop()     )  # pop stack & add to Postfix
								stack.append(__lowerCAmelCase     )  # push x to stack

				print(
				    x.center(8     )   ,							(''''''.join(__lowerCAmelCase     )).ljust(__lowerCAmelCase     )   ,							(''''''.join(__lowerCAmelCase     )).ljust(__lowerCAmelCase     )   ,							sep=''' | '''   ,							)  # Output in tabular format

		while len(__lowerCAmelCase     ) > 0:  # while stack is not empty
				post_fix.append(stack.pop()     )  # pop stack & add to Postfix
				print(
				    ''' '''.center(8     )   ,							(''''''.join(__lowerCAmelCase     )).ljust(__lowerCAmelCase     )   ,							(''''''.join(__lowerCAmelCase     )).ljust(__lowerCAmelCase     )   ,							sep=''' | '''   ,							)  # Output in tabular format

		return "".join(__lowerCAmelCase     )  # return Postfix as str




def 	_a       (				__lowerCAmelCase		:      List[str]     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  list(infix[::-1]     )  # reverse the infix equation

		for i in range(len(__lowerCAmelCase     )     ):
				if infix[i] == "(":
						snake_case__						:       List[str]       						=  ')'  # change "(" to ")"
				elif infix[i] == ")":
						snake_case__						:       Any       						=  '('  # change ")" to "("

		return (infix_2_postfix(''''''.join(__lowerCAmelCase     )     ))[
		    ::-1
		]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
					lowerCAmelCase__						:       List[str]										=						input("""\nEnter an Infix Equation = """)  # Input an Infix equation
					lowerCAmelCase__						:       Union[str, Any]										=						''''''.join(Infix.split())  # Remove spaces from the input
					print("""\n\t""", Infix, """(Infix) -> """, infix_2_prefix(Infix), """(Prefix)""")






'''simple docstring'''


import argparse
import os
import shutil

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




def 	_a       (				__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		snake_case__						:       int       						=  args.pruning_method
		snake_case__						:       Dict       						=  args.threshold

		snake_case__						:       str       						=  args.model_name_or_path.rstrip('''/'''     )
		snake_case__						:       int       						=  args.target_model_path

		print(F"""Load fine-pruned model from {model_name_or_path}"""     )
		snake_case__						:       Dict       						=  torch.load(os.path.join(__lowerCAmelCase   ,							'''pytorch_model.bin'''     )     )
		snake_case__						:       Any       						=  {}

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

		if target_model_path is None:
				snake_case__						:       Optional[int]       						=  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__						:       str										=						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)



