


import argparse
import os
import shutil

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
     __UpperCamelCase   							=args.pruning_method
     __UpperCamelCase   							=args.threshold

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

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

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

     if target_model_path is None:
          __UpperCamelCase   							=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__":
   _A			=						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',
   )

   _A			=						parser.parse_args()

   main(args)









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_A			=						logging.get_logger(__name__)


if is_vision_available():
   import PIL






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  ["pixel_values"]






    def __init__(      self ,       A_ = True ,       A_ = None ,       A_ = PILImageResampling.BICUBIC ,       A_ = True ,       A_ = None ,       A_ = True ,       A_ = 1 / 255 ,       A_ = True ,       A_ = None ,       A_ = None ,       A_ = True ,       **A_ ,       )  ->	None:
         super().__init__(**A_   )
         __UpperCamelCase   							=size if size is not None else {'shortest_edge': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         __UpperCamelCase   							=crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_ ,       param_name='crop_size'   )

         __UpperCamelCase   							=do_resize
         __UpperCamelCase   							=size
         __UpperCamelCase   							=resample
         __UpperCamelCase   							=do_center_crop
         __UpperCamelCase   							=crop_size
         __UpperCamelCase   							=do_rescale
         __UpperCamelCase   							=rescale_factor
         __UpperCamelCase   							=do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         __UpperCamelCase   							=image_std if image_std is not None else OPENAI_CLIP_STD
         __UpperCamelCase   							=do_convert_rgb






    def _a (      self ,       A_ ,       A_ ,       A_ = PILImageResampling.BICUBIC ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
         __UpperCamelCase   							=get_resize_output_image_size(A_ ,       size=size['shortest_edge'] ,       default_to_square=A_   )
         return resize(A_ ,       size=A_ ,       resample=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_   )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
         return center_crop(A_ ,       size=(size['height'], size['width']) ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	Union[str, Any]:
         return rescale(A_ ,       scale=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         return normalize(A_ ,       mean=A_ ,       std=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = ChannelDimension.FIRST ,       **A_ ,       )  ->	PIL.Image.Image:
         __UpperCamelCase   							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase   							=size if size is not None else self.size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='size' ,       default_to_square=A_   )
         __UpperCamelCase   							=resample if resample is not None else self.resample
         __UpperCamelCase   							=do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase   							=crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='crop_size' ,       default_to_square=A_   )
         __UpperCamelCase   							=do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase   							=rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase   							=do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase   							=image_std if image_std is not None else self.image_std
         __UpperCamelCase   							=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __UpperCamelCase   							=make_list_of_images(A_   )

         if not valid_images(A_   ):
              raise ValueError(
                  'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                  'torch.Tensor, tf.Tensor or jax.ndarray.'   )

         if do_resize and size is None:
              raise ValueError('Size must be specified if do_resize is True.'   )

         if do_center_crop and crop_size is None:
              raise ValueError('Crop size must be specified if do_center_crop is True.'   )

         if do_rescale and rescale_factor is None:
              raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              __UpperCamelCase   							=[convert_to_rgb(A_   ) for image in images]

         # All transformations expect numpy arrays.
         __UpperCamelCase   							=[to_numpy_array(A_   ) for image in images]

         if do_resize:
              __UpperCamelCase   							=[self.resize(image=A_ ,       size=A_ ,       resample=A_   ) for image in images]

         if do_center_crop:
              __UpperCamelCase   							=[self.center_crop(image=A_ ,       size=A_   ) for image in images]

         if do_rescale:
              __UpperCamelCase   							=[self.rescale(image=A_ ,       scale=A_   ) for image in images]

         if do_normalize:
              __UpperCamelCase   							=[self.normalize(image=A_ ,       mean=A_ ,       std=A_   ) for image in images]

         __UpperCamelCase   							=[to_channel_dimension_format(A_ ,       A_   ) for image in images]

         __UpperCamelCase   							={'pixel_values': images}
         return BatchFeature(data=A_ ,       tensor_type=A_   )





