





'''simple docstring'''

import argparse
import os
import shutil

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







def  a__    (    lowercase :					int				)		->		str:





     """simple docstring"""

     _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(lowercase, '''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=lowercase, threshold=lowercase				)
                    _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(lowercase, lowercase				)
                    _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(lowercase, lowercase, lowercase				)
                    _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(lowercase				)
                    _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(lowercase				), F"""bertarized_{os.path.basename(lowercase				)}"""				)

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

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


if __name__ == "__main__":
   lowercase__						:      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',
   )

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

   main(args)










'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__						:      List[Any]	=    logging.get_logger(__name__)

lowercase__						:      Tuple	=    {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					int						    =   'trocr'
       _snake_case     :					List[str]						    =   ['past_key_values']
       _snake_case     :					Tuple						    =   {
           'num_attention_heads': 'decoder_attention_heads',
           'hidden_size': 'd_model',
           'num_hidden_layers': 'decoder_layers',
       }





       def __init__(							self       :		Tuple	,			lowerCAmelCase__       :		Union[str, Any]=50265	,			lowerCAmelCase__       :		List[Any]=1024	,			lowerCAmelCase__       :		List[str]=12	,			lowerCAmelCase__       :		int=16	,			lowerCAmelCase__       :		List[Any]=4096	,			lowerCAmelCase__       :		Optional[int]="gelu"	,			lowerCAmelCase__       :		int=512	,			lowerCAmelCase__       :		Optional[Any]=0.1	,			lowerCAmelCase__       :		Optional[int]=0.0	,			lowerCAmelCase__       :		Dict=0.0	,			lowerCAmelCase__       :		Tuple=2	,			lowerCAmelCase__       :		Dict=0.02	,			lowerCAmelCase__       :		Tuple=0.0	,			lowerCAmelCase__       :		int=True	,			lowerCAmelCase__       :		Optional[Any]=False	,			lowerCAmelCase__       :		Any=True	,			lowerCAmelCase__       :		Dict=True	,			lowerCAmelCase__       :		Optional[int]=1	,			lowerCAmelCase__       :		List[Any]=0	,			lowerCAmelCase__       :		Any=2	,			**lowerCAmelCase__       :		Optional[int]	,			)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	vocab_size
            _UpperCamelCase       =	d_model
            _UpperCamelCase       =	decoder_layers
            _UpperCamelCase       =	decoder_attention_heads
            _UpperCamelCase       =	decoder_ffn_dim
            _UpperCamelCase       =	activation_function
            _UpperCamelCase       =	max_position_embeddings
            _UpperCamelCase       =	dropout
            _UpperCamelCase       =	attention_dropout
            _UpperCamelCase       =	activation_dropout
            _UpperCamelCase       =	init_std
            _UpperCamelCase       =	decoder_layerdrop
            _UpperCamelCase       =	use_cache
            _UpperCamelCase       =	scale_embedding
            _UpperCamelCase       =	use_learned_position_embeddings
            _UpperCamelCase       =	layernorm_embedding

            super().__init__(
                pad_token_id=lowerCAmelCase__	,			bos_token_id=lowerCAmelCase__	,			eos_token_id=lowerCAmelCase__	,			decoder_start_token_id=lowerCAmelCase__	,			**lowerCAmelCase__	,			)



