





import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Optional[Any]:
    lowerCamelCase__					:   Dict      				=	args.pruning_method
    lowerCamelCase__					:   List[Any]      				=	args.threshold

    lowerCamelCase__					:   List[str]      				=	args.model_name_or_path.rstrip('/'				)
    lowerCamelCase__					:   str      				=	args.target_model_path

    print(F"""Load fine-pruned model from {model_name_or_path}"""				)
    lowerCamelCase__					:   Optional[Any]      				=	torch.load(os.path.join(__lowerCAmelCase		,			'pytorch_model.bin'				)				)
    lowerCamelCase__					:   List[Any]      				=	{}

    for name, tensor in model.items():
        if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
            lowerCamelCase__					:   str      				=	tensor
            print(F"""Copied layer {name}"""				)
        elif "classifier" in name or "qa_output" in name:
            lowerCamelCase__					:   Union[str, Any]      				=	tensor
            print(F"""Copied layer {name}"""				)
        elif "bias" in name:
            lowerCamelCase__					:   Tuple      				=	tensor
            print(F"""Copied layer {name}"""				)
        else:
            if pruning_method == "magnitude":
                lowerCamelCase__					:   Optional[Any]      				=	MagnitudeBinarizer.apply(inputs=__lowerCAmelCase		,			threshold=__lowerCAmelCase				)
                lowerCamelCase__					:   List[Any]      				=	tensor * mask
                print(F"""Pruned layer {name}"""				)
            elif pruning_method == "topK":
                if "mask_scores" in name:
                    continue
                lowerCamelCase__					:   Union[str, Any]      				=	name[:-6]
                lowerCamelCase__					:   List[str]      				=	model[F"""{prefix_}mask_scores"""]
                lowerCamelCase__					:   Tuple      				=	TopKBinarizer.apply(__lowerCAmelCase		,			__lowerCAmelCase				)
                lowerCamelCase__					:   List[Any]      				=	tensor * mask
                print(F"""Pruned layer {name}"""				)
            elif pruning_method == "sigmoied_threshold":
                if "mask_scores" in name:
                    continue
                lowerCamelCase__					:   Union[str, Any]      				=	name[:-6]
                lowerCamelCase__					:   str      				=	model[F"""{prefix_}mask_scores"""]
                lowerCamelCase__					:   List[Any]      				=	ThresholdBinarizer.apply(__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase				)
                lowerCamelCase__					:   Tuple      				=	tensor * mask
                print(F"""Pruned layer {name}"""				)
            elif pruning_method == "l0":
                if "mask_scores" in name:
                    continue
                lowerCamelCase__					:   str      				=	name[:-6]
                lowerCamelCase__					:   List[Any]      				=	model[F"""{prefix_}mask_scores"""]
                lowerCamelCase__					:   Any      				=	-0.1, 1.1
                lowerCamelCase__					:   Any      				=	torch.sigmoid(__lowerCAmelCase				)
                lowerCamelCase__					:   Optional[int]      				=	s * (r - l) + l
                lowerCamelCase__					:   List[Any]      				=	s_bar.clamp(min=0.0		,			max=1.0				)
                lowerCamelCase__					:   List[Any]      				=	tensor * mask
                print(F"""Pruned layer {name}"""				)
            else:
                raise ValueError('Unknown pruning method'				)

    if target_model_path is None:
        lowerCamelCase__					:   List[Any]      				=	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__":
    _UpperCAmelCase       : 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""",
    )

    _UpperCAmelCase       : Union[str, Any]     =       parser.parse_args()

    main(args)












from __future__ import annotations

from collections.abc import Generator


def  SCREAMING_SNAKE_CASE   ( )    ->  Generator[int, None, None]:
    lowerCamelCase__					:   dict[int, int]      				=	{}
    lowerCamelCase__					:   Union[str, Any]      				=	2
    while True:
        lowerCamelCase__					:   Optional[int]      				=	factor_map.pop(_UpperCAmelCase		,			_UpperCAmelCase				)
        if factor:
            lowerCamelCase__					:   Optional[Any]      				=	factor + prime
            while x in factor_map:
                x += factor
            lowerCamelCase__					:   Optional[Any]      				=	factor
        else:
            lowerCamelCase__					:   Union[str, Any]      				=	prime
            yield prime
        prime += 1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 1e10				)    ->  int:
    lowerCamelCase__					:   Tuple      				=	sieve()

    lowerCamelCase__					:   Dict      				=	1
    while True:
        lowerCamelCase__					:   List[Any]      				=	next(_UpperCAmelCase				)
        if (2 * prime * n) > limit:
            return n
        # Ignore the next prime as the reminder will be 2.
        next(_UpperCAmelCase				)
        n += 2


if __name__ == "__main__":
    print(solution())





