
"""simple docstring"""
def 				lowercase      (_lowerCAmelCase		):
						# must be
						__lowerCAmelCase        =       [list(_lowerCAmelCase		) for row in matrix]
						if matrix and isinstance(_lowerCAmelCase				,  _lowerCAmelCase		):
												if isinstance(matrix[0]				,  _lowerCAmelCase		):
																		__lowerCAmelCase        =       0
																		for row in matrix:
																								if prev_len == 0:
																														__lowerCAmelCase        =       len(_lowerCAmelCase		)
																														__lowerCAmelCase        =       True
																								else:
																														__lowerCAmelCase        =       prev_len == len(_lowerCAmelCase		)
												else:
																		__lowerCAmelCase        =       True
						else:
												__lowerCAmelCase        =       False

						return result

def 				lowercase      (_lowerCAmelCase		):
						if check_matrix(_lowerCAmelCase		) and len(_lowerCAmelCase		) > 0:
												__lowerCAmelCase        =       [list(_lowerCAmelCase		) for row in a]
												__lowerCAmelCase        =       len(_lowerCAmelCase		)
												if isinstance(a[0]				,  _lowerCAmelCase		):
																		__lowerCAmelCase        =       len(a[0]		)
												else:
																		for dat in a:
																								print(_lowerCAmelCase		)
																		return

												# horizotal printing increasing
												for i in range(0				,  _lowerCAmelCase		):
																		print(a[0][i]		)
												# vertical printing down
												for i in range(1				,  _lowerCAmelCase		):
																		print(a[i][mat_col - 1]		)
												# horizotal printing decreasing
												if mat_row > 1:
																		for i in range(mat_col - 2				,  -1				,  -1		):
																								print(a[mat_row - 1][i]		)
        # vertical printing up
												for i in range(mat_row - 2				,  0				,  -1		):
																		print(a[i][0]		)
												__lowerCAmelCase        =       [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
												if len(_lowerCAmelCase		) > 0:
																		spiral_print_clockwise(_lowerCAmelCase		)
												else:
																		return
						else:
												print("""Not a valid matrix"""		)
												return

def 				lowercase      (_lowerCAmelCase		):
						if matrix:
												return list(matrix.pop(0		)		) + spiral_traversal(list(zip(*_lowerCAmelCase		)		)[::-1]		)
						else:
												return []


# driver code
if __name__ == "__main__":
			import doctest

			doctest.testmod()

			SCREAMING_SNAKE_CASE_			      =					[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
			spiral_print_clockwise(a)


"""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)
