

'''simple docstring'''


from PIL import Image




def 	_a       (				__lowerCAmelCase		:      Image   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  (2_59 * (level + 2_55)) / (2_55 * (2_59 - level))

		def contrast(__lowerCAmelCase		:      int     ) -> int:
				return int(1_28 + factor * (c - 1_28)     )

		return img.point(__lowerCAmelCase     )


if __name__ == "__main__":
					# Load image
					with Image.open("""image_data/lena.jpg""") as img:
										# Change contrast to 170
										lowerCAmelCase__						:       str										=						change_contrast(img, 170)
										cont_img.save("""image_data/lena_high_contrast.png""", format="""png""")






'''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)



