
"""simple docstring"""
from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available

def 				lowercase      (_lowerCAmelCase		):
						if is_torch_available():
												import torch

												if isinstance(_lowerCAmelCase				,  torch.Tensor		):
																		return True

						return isinstance(_lowerCAmelCase				,  np.ndarray		)



class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       fields(self							)

													# Safety and consistency checks
													if not len(snake_case_							):
																			raise ValueError(f"""{self.__class__.__name__} has no fields."""							)

													__lowerCAmelCase        =       getattr(self  ,							class_fields[0].name							)
													__lowerCAmelCase        =       all(getattr(self  ,							field.name							) is None for field in class_fields[1:]							)

													if other_fields_are_none and isinstance(snake_case_  ,							snake_case_							):
																			for key, value in first_field.items():
																									__lowerCAmelCase        =       value
													else:
																			for field in class_fields:
																									__lowerCAmelCase        =       getattr(self  ,							field.name							)
																									if v is not None:
																															__lowerCAmelCase        =       v




							def __delitem__(				self  ,							*snake_case_  ,							**snake_case_							)   ->       Optional[int]:
													raise Exception(f"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""							)




							def  A__		(				self  ,							*snake_case_  ,							**snake_case_							)   ->       Union[str, Any]:
													raise Exception(f"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""							)




							def  A__		(				self  ,							*snake_case_  ,							**snake_case_							)   ->       List[Any]:
													raise Exception(f"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""							)




							def  A__		(				self  ,							*snake_case_  ,							**snake_case_							)   ->       Dict:
													raise Exception(f"""You cannot use ``update`` on a {self.__class__.__name__} instance."""							)




							def __getitem__(				self  ,							snake_case_							)   ->       Optional[int]:
													if isinstance(snake_case_  ,							snake_case_							):
																			__lowerCAmelCase        =       dict(self.items()							)
																			return inner_dict[k]
													else:
																			return self.to_tuple()[k]




							def __setattr__(				self  ,							snake_case_  ,							snake_case_							)   ->       int:
													if name in self.keys() and value is not None:
																			# Don't call self.__setitem__ to avoid recursion errors
																			super().__setitem__(snake_case_  ,							snake_case_							)
													super().__setattr__(snake_case_  ,							snake_case_							)




							def __setitem__(				self  ,							snake_case_  ,							snake_case_							)   ->       Tuple:
													# Will raise a KeyException if needed
													super().__setitem__(snake_case_  ,							snake_case_							)
													# Don't call self.__setattr__ to avoid recursion errors
													super().__setattr__(snake_case_  ,							snake_case_							)




							def  A__		(				self							)   ->       Tuple[Any]:
													return tuple(self[k] for k in self.keys()							)


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

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

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

						__lowerCAmelCase        =       torch.load(os.path.join(_lowerCAmelCase				,  """pytorch_model.bin"""		)				,  map_location="""cpu"""		)

						__lowerCAmelCase        =       0  # Number of remaining (not pruned) params in the encoder
						__lowerCAmelCase        =       0  # Number of params in the encoder

						print("""name""".ljust(60				,  """ """		)				,  """Remaining Weights %"""				,  """Remaining Weight"""		)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								__lowerCAmelCase        =       TopKBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__lowerCAmelCase        =       ThresholdBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "l0":
																								__lowerCAmelCase ,      __lowerCAmelCase        =       -0.1, 1.1
																								__lowerCAmelCase        =       torch.sigmoid(_lowerCAmelCase		)
																								__lowerCAmelCase        =       s * (r - l) + l
																								__lowerCAmelCase        =       s_bar.clamp(min=0.0				,  max=1.0		)
																								__lowerCAmelCase        =       (mask > 0.0).sum().item()
																		else:
																								raise ValueError("""Unknown pruning method"""		)
																		remaining_count += mask_ones
																		print(name.ljust(60				,  """ """		)				,  str(round(100 * mask_ones / param.numel()				,  3		)		).ljust(20				,  """ """		)				,  str(_lowerCAmelCase		)		)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""""""		)
						print("""Remaining Weights (global) %: """				,  100 * remaining_count / encoder_count		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_			      =					argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
			        ''' pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `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(
			    '''--serialization_dir''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			SCREAMING_SNAKE_CASE_			      =					parser.parse_args()

			main(args)
