
"""simple docstring"""
import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase=256		):
						__lowerCAmelCase        =       min(img.size		)

						if s < target_image_size:
												raise ValueError(f"""min dim for image {s} < {target_image_size}"""		)

						__lowerCAmelCase        =       target_image_size / s
						__lowerCAmelCase        =       (round(r * img.size[1]		), round(r * img.size[0]		))
						__lowerCAmelCase        =       TF.resize(_lowerCAmelCase				,  _lowerCAmelCase				,  interpolation=PIL.Image.LANCZOS		)
						__lowerCAmelCase        =       TF.center_crop(_lowerCAmelCase				,  output_size=2 * [target_image_size]		)
						__lowerCAmelCase        =       torch.unsqueeze(T.ToTensor()(_lowerCAmelCase		)				,  0		)
						return img

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       2.0 * x - 1.0
						return x

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase=True				,  _lowerCAmelCase="RGB"		):
						__lowerCAmelCase        =       x.detach().cpu()
						if process:
												__lowerCAmelCase        =       post_process_tensor(_lowerCAmelCase		)
						__lowerCAmelCase        =       x.numpy()
						if process:
												__lowerCAmelCase        =       (255 * x).astype(np.uinta		)
						__lowerCAmelCase        =       Image.fromarray(_lowerCAmelCase		)
						if not x.mode == mode:
												__lowerCAmelCase        =       x.convert(_lowerCAmelCase		)
						return x

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       torch.clamp(_lowerCAmelCase				,  -1.0				,  1.0		)
						__lowerCAmelCase        =       (x + 1.0) / 2.0
						__lowerCAmelCase        =       x.permute(1				,  2				,  0		)
						return x



def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       post_process_tensor(x.squeeze()		)
						return x.permute(2				,  0				,  1		).unsqueeze(0		)


"""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)
