

'''simple docstring'''



import argparse
import os
import shutil

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


def       snake_case   (	snake_case      : Any			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							args.pruning_method
    lowerCAmelCase 						=							args.threshold

    lowerCAmelCase 						=							args.model_name_or_path.rstrip('/'			)
    lowerCAmelCase 						=							args.target_model_path

    print(F'Load fine-pruned model from {model_name_or_path}'			)
    lowerCAmelCase 						=							torch.load(os.path.join(snake_case					,		'pytorch_model.bin'			)			)
    lowerCAmelCase 						=							{}

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

    if target_model_path is None:
        lowerCAmelCase 						=							os.path.join(
            os.path.dirname(snake_case			)					,		F'bertarized_{os.path.basename(snake_case			)}'			)

    if not os.path.isdir(snake_case			):
        shutil.copytree(snake_case					,		snake_case			)
        print(F'\nCreated folder {target_model_path}'			)

    torch.save(snake_case					,		os.path.join(snake_case					,		'pytorch_model.bin'			)			)
    print('\nPruned model saved! See you later!'			)


if __name__ == "__main__":
     _UpperCamelCase						:  Optional[Any]							       =				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						:  Any							       =				parser.parse_args()

     main(args)








'''simple docstring'''



def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    # edges = list of graph's edges
    lowerCAmelCase 						=							get_edges(snake_case			)

    # While there are still elements in edges list, take an arbitrary edge
    # (from_node, to_node) and add his extremity to chosen_vertices and then
    # remove all arcs adjacent to the from_node and to_node
    while edges:
        lowerCAmelCase						,   lowerCAmelCase 						=							edges.pop()
        chosen_vertices.add(snake_case			)
        chosen_vertices.add(snake_case			)
        for edge in edges.copy():
            if from_node in edge or to_node in edge:
                edges.discard(snake_case			)
    return chosen_vertices


def       snake_case   (	snake_case      : dict			) ->  set:


    """simple docstring"""


    lowerCAmelCase 						=							set()
    for from_node, to_nodes in graph.items():
        for to_node in to_nodes:
            edges.add((from_node, to_node)			)
    return edges


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
     # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





