



'''simple docstring'''







import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Dict						) ->							Union[str, Any]:
							lowerCAmelCase__  =   args.serialization_dir
							lowerCAmelCase__  =   args.pruning_method
							lowerCAmelCase__  =   args.threshold

							lowerCAmelCase__  =   torch.load(os.path.join(UpperCAmelCase_					, """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(UpperCAmelCase_					, UpperCAmelCase_						).sum().item()
																					elif pruning_method == "sigmoied_threshold":
																												lowerCAmelCase__  =   ThresholdBinarizer.apply(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_						).sum().item()
																					elif pruning_method == "l0":
																												lowerCAmelCase__  ,lowerCAmelCase__  =   -0.1, 1.1
																												lowerCAmelCase__  =   torch.sigmoid(UpperCAmelCase_						)
																												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(UpperCAmelCase_						)						)
														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__":
		_UpperCamelCase  						=			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""",
		)

		_UpperCamelCase  						=			parser.parse_args()

		main(args)










'''simple docstring'''







def 			_lowerCAmelCase(      UpperCAmelCase_      :	str						) ->							str:
							lowerCAmelCase__  =   word.upper()
							lowerCAmelCase__  =   0
							lowerCAmelCase__  =   {}
							lowerCAmelCase__  =   []

							for letter in word:
														if letter not in letter_nums:
																					lowerCAmelCase__  =   str(UpperCAmelCase_						)
																					next_num += 1
														word_pattern.append(letter_nums[letter]						)
							return ".".join(UpperCAmelCase_						)


if __name__ == "__main__":
		import pprint
		import time

		_UpperCamelCase  						=			time.time()
		with open("""dictionary.txt""") as in_file:
				_UpperCamelCase  						=			in_file.read().splitlines()

		_UpperCamelCase  						=			{}
		for word in word_list:
				_UpperCamelCase  						=			get_word_pattern(word)
				if pattern in all_patterns:
						all_patterns[pattern].append(word)
				else:
						_UpperCamelCase  						=			[word]

		with open("""word_patterns.txt""", """w""") as out_file:
				out_file.write(pprint.pformat(all_patterns))

		_UpperCamelCase  						=			round(time.time() - start_time, 2)
		print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
		# Done!  9,581 word patterns found in 0.58 seconds.





