

import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]				)							->				Optional[int]:





    '''simple docstring'''






    A__   				=       args.pruning_method
    A__   				=       args.threshold

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

    print(f'Load fine-pruned model from {model_name_or_path}'				)
    A__   				=       torch.load(os.path.join(SCREAMING_SNAKE_CASE__   ,      'pytorch_model.bin'				)				)
    A__   				=       {}

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

    if target_model_path is None:
        A__   				=       os.path.join(
            os.path.dirname(SCREAMING_SNAKE_CASE__				)   ,      f'bertarized_{os.path.basename(SCREAMING_SNAKE_CASE__				)}'				)

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

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


if __name__ == "__main__":
     lowercase_			=  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",
     )

     lowercase_			=  parser.parse_args()

     main(args)




import comet  # From: unbabel-comet
import torch

import datasets


lowercase_			=  datasets.logging.get_logger(__name__)

lowercase_			=  "\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

lowercase_			=  "\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

lowercase_			=  "\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,							_KWARGS_DESCRIPTION			)
class 			A						(							datasets.Metric			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Optional[Any]		)->   str:





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,homepage='https://unbabel.github.io/COMET/html/index.html',inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'sources': datasets.Value('string',id='sequence'		),
                    'predictions': datasets.Value('string',id='sequence'		),
                    'references': datasets.Value('string',id='sequence'		),
                }		),codebase_urls=['https://github.com/Unbabel/COMET'],reference_urls=[
                'https://github.com/Unbabel/COMET',
                'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
                'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
            ],)


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Dict		)->   str:





        '''simple docstring'''







        if self.config_name == "default":
            A__   				=       comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'		)		)
        else:
            A__   				=       comet.load_from_checkpoint(comet.download_model(self.config_name		)		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Dict,lowercase_			:	int,lowercase_			:	List[Any],lowercase_			:	Tuple=None,lowercase_			:	List[str]=False		)->   int:





        '''simple docstring'''







        if gpus is None:
            A__   				=       1 if torch.cuda.is_available() else 0
        A__   				=       {'src': sources, 'mt': predictions, 'ref': references}
        A__   				=       [dict(zip(lowercase_,lowercase_		)		) for t in zip(*data.values()		)]
        A__				,						A__   				=       self.scorer.predict(lowercase_,gpus=lowercase_,progress_bar=lowercase_		)
        return {"mean_score": mean_score, "scores": scores}

