


'''simple docstring'''




import argparse
import os
import shutil

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


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   _a					:  Dict						=  args.pruning_method
   _a					:  Optional[Any]						=  args.threshold

   _a					:  Union[str, Any]						=  args.model_name_or_path.rstrip('/'      )
   _a					:  Dict						=  args.target_model_path

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

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

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

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

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


if __name__ == "__main__":
 UpperCAmelCase_      :		str          =		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",
 )

 UpperCAmelCase_      :		Optional[Any]          =		parser.parse_args()

 main(args)






'''simple docstring'''




import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase_      :		List[str]          =		logging.get_logger(__name__)
logging.set_verbosity_info()


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   if "xprophetnet" in prophetnet_checkpoint_path:
      _a					:  str						=  XLMProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Any						=  XLMProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )
   else:
      _a					:  Optional[Any]						=  ProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Dict						=  ProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )

   _a					:  Tuple						=  ['key_proj', 'value_proj', 'query_proj']

   _a					:  str						=  {
       'self_attn': 'ngram_self_attn',
       'cross_attn': 'encoder_attn',
       'cross_attn_layer_norm': 'encoder_attn_layer_norm',
       'feed_forward_layer_norm': 'final_layer_norm',
       'feed_forward': '',
       'intermediate': 'fc1',
       'output': 'fc2',
       'key_proj': 'k_proj',
       'query_proj': 'q_proj',
       'value_proj': 'v_proj',
       'word_embeddings': 'embed_tokens',
       'embeddings_layer_norm': 'emb_layer_norm',
       'relative_pos_embeddings': 'relative_linear',
       'ngram_embeddings': 'ngram_input_embed',
       'position_embeddings': 'embed_positions',
   }

   for key in loading_info["missing_keys"]:
      _a					:  Tuple						=  key.split('.'      )

      if attributes[0] == "lm_head":
         _a					:  str						=  prophet
         _a					:  Dict						=  prophet_old
      else:
         _a					:  Union[str, Any]						=  prophet.prophetnet
         _a					:  List[Any]						=  prophet_old.model

      _a					:  Dict						=  False
      for attribute in attributes:
         if attribute in mapping:
            _a					:  List[str]						=  mapping[attribute]
            if not hasattr(A		,	A      ) and len(A      ) > 0:
               _a					:  Any						=  attribute
         elif hasattr(A		,	A      ):
            _a					:  Dict						=  attribute

         if attribute == "weight":
            assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
            _a					:  str						=  old_model.weight
            logger.info(f'''{attribute} is initialized.'''      )
            _a					:  List[str]						=  True
            break
         elif attribute == "bias":
            assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
            _a					:  int						=  old_model.bias
            logger.info(f'''{attribute} is initialized'''      )
            _a					:  Optional[int]						=  True
            break
         elif attribute in special_keys and hasattr(A		,	'in_proj_weight'      ):
            _a					:  Optional[int]						=  old_model.in_proj_weight.shape[0] // 3
            _a					:  List[str]						=  getattr(A		,	A      )
            param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
            param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
            if attribute == "query_proj":
               _a					:  Optional[int]						=  nn.Parameter(old_model.in_proj_weight[:embed_dim, :]      )
               _a					:  Union[str, Any]						=  nn.Parameter(old_model.in_proj_bias[:embed_dim]      )

            elif attribute == "key_proj":
               _a					:  str						=  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
               _a					:  List[Any]						=  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
            elif attribute == "value_proj":
               _a					:  Optional[int]						=  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
               _a					:  str						=  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
            _a					:  int						=  True
            break
         elif attribute == "position_embeddings":
            assert (
                model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
            ), "Hidden size has to match"
            assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
            _a					:  Tuple						=  nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]      )
            _a					:  Any						=  True
            break

         if attribute.isdigit():
            _a					:  List[str]						=  model[int(A      )]
            _a					:  Union[str, Any]						=  old_model[int(A      )]
         else:
            _a					:  List[Any]						=  getattr(A		,	A      )

            if old_attribute == "":
               _a					:  List[str]						=  old_model
            else:
               if not hasattr(A		,	A      ):
                  raise ValueError(f'''{old_model} does not have {old_attribute}'''      )
               _a					:  Optional[int]						=  getattr(A		,	A      )

      if not is_key_init:
         raise ValueError(f'''{key} was not correctly initialized!'''      )

   print(f'''Saving model to {pytorch_dump_folder_path}'''      )
   prophet.save_pretrained(A      )


if __name__ == "__main__":
 UpperCAmelCase_      :		Any          =		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 UpperCAmelCase_      :		List[str]          =		parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


