


"""simple docstring"""







import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[str]								=							args.pruning_method
  lowerCAmelCase					:					List[Any]								=							args.threshold

  lowerCAmelCase					:					str								=							args.model_name_or_path.rstrip("/"    )
  lowerCAmelCase					:					Optional[int]								=							args.target_model_path

  print(f"""Load fine-pruned model from {model_name_or_path}"""    )
  lowerCAmelCase					:					int								=							torch.load(os.path.join(SCREAMING_SNAKE_CASE    ,				"pytorch_model.bin"    )    )
  lowerCAmelCase					:					List[str]								=							{}

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

  if target_model_path is None:
    lowerCAmelCase					:					str								=							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__":
     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)









"""simple docstring"""







from ...configuration_utils import PretrainedConfig


lowerCAmelCase__				    =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Any       ="tapas"

       def __init__(						self		,					snake_case__=30_522		,					snake_case__=768		,					snake_case__=12		,					snake_case__=12		,					snake_case__=3_072		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=1_024		,					snake_case__=[3, 256, 256, 2, 256, 256, 10]		,					snake_case__=0.02		,					snake_case__=1e-12		,					snake_case__=0		,					snake_case__=10.0		,					snake_case__=0		,					snake_case__=1.0		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=False		,					snake_case__="ratio"		,					snake_case__=None		,					snake_case__=None		,					snake_case__=64		,					snake_case__=32		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=None		,					snake_case__=None		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(pad_token_id=snake_case__		,					**snake_case__						)

         # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
         lowerCAmelCase					:					List[Any]								=							vocab_size
         lowerCAmelCase					:					List[str]								=							hidden_size
         lowerCAmelCase					:					Optional[Any]								=							num_hidden_layers
         lowerCAmelCase					:					List[str]								=							num_attention_heads
         lowerCAmelCase					:					Optional[Any]								=							hidden_act
         lowerCAmelCase					:					List[Any]								=							intermediate_size
         lowerCAmelCase					:					Optional[Any]								=							hidden_dropout_prob
         lowerCAmelCase					:					str								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Any								=							max_position_embeddings
         lowerCAmelCase					:					Dict								=							type_vocab_sizes
         lowerCAmelCase					:					Union[str, Any]								=							initializer_range
         lowerCAmelCase					:					str								=							layer_norm_eps

         # Fine-tuning task hyperparameters
         lowerCAmelCase					:					Dict								=							positive_label_weight
         lowerCAmelCase					:					Union[str, Any]								=							num_aggregation_labels
         lowerCAmelCase					:					Optional[Any]								=							aggregation_loss_weight
         lowerCAmelCase					:					List[Any]								=							use_answer_as_supervision
         lowerCAmelCase					:					Dict								=							answer_loss_importance
         lowerCAmelCase					:					List[Any]								=							use_normalized_answer_loss
         lowerCAmelCase					:					List[str]								=							huber_loss_delta
         lowerCAmelCase					:					Optional[int]								=							temperature
         lowerCAmelCase					:					Optional[int]								=							aggregation_temperature
         lowerCAmelCase					:					Any								=							use_gumbel_for_cells
         lowerCAmelCase					:					Union[str, Any]								=							use_gumbel_for_aggregation
         lowerCAmelCase					:					Union[str, Any]								=							average_approximation_function
         lowerCAmelCase					:					int								=							cell_selection_preference
         lowerCAmelCase					:					Dict								=							answer_loss_cutoff
         lowerCAmelCase					:					Optional[int]								=							max_num_rows
         lowerCAmelCase					:					Union[str, Any]								=							max_num_columns
         lowerCAmelCase					:					Any								=							average_logits_per_cell
         lowerCAmelCase					:					List[Any]								=							select_one_column
         lowerCAmelCase					:					Tuple								=							allow_empty_column_selection
         lowerCAmelCase					:					str								=							init_cell_selection_weights_to_zero
         lowerCAmelCase					:					List[Any]								=							reset_position_index_per_cell
         lowerCAmelCase					:					Optional[Any]								=							disable_per_token_loss

         # Aggregation hyperparameters
         lowerCAmelCase					:					List[str]								=							aggregation_labels
         lowerCAmelCase					:					List[str]								=							no_aggregation_label_index

         if isinstance(self.aggregation_labels		,					snake_case__						):
           lowerCAmelCase					:					Union[str, Any]								=							{int(snake_case__						): v for k, v in aggregation_labels.items()}





