import argparse
import os
import shutil

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       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(lowerCAmelCase__     ,  '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=lowerCAmelCase__     ,  threshold=lowerCAmelCase__     )
                            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(lowerCAmelCase__     ,  lowerCAmelCase__     )
                            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(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                            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(lowerCAmelCase__     )
                            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(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__     						=			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__     						=			parser.parse_args()

    main(args)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase__     						=			{
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'AdaptiveEmbedding',
        'TransfoXLForSequenceClassification',
        'TransfoXLLMHeadModel',
        'TransfoXLModel',
        'TransfoXLPreTrainedModel',
        'load_tf_weights_in_transfo_xl',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFAdaptiveEmbedding',
        'TFTransfoXLForSequenceClassification',
        'TFTransfoXLLMHeadModel',
        'TFTransfoXLMainLayer',
        'TFTransfoXLModel',
        'TFTransfoXLPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
    from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_transfo_xl import (
            TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            AdaptiveEmbedding,
            TransfoXLForSequenceClassification,
            TransfoXLLMHeadModel,
            TransfoXLModel,
            TransfoXLPreTrainedModel,
            load_tf_weights_in_transfo_xl,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_transfo_xl import (
            TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAdaptiveEmbedding,
            TFTransfoXLForSequenceClassification,
            TFTransfoXLLMHeadModel,
            TFTransfoXLMainLayer,
            TFTransfoXLModel,
            TFTransfoXLPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__     						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
