
import copy

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


_lowerCamelCase        =logging.get_logger(__name__)

_lowerCamelCase        ={}





class 							A__		(     __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase    :   Union[str, Any]   	=      """fsmt_decoder"""
      def __init__(				self   ,  __magic_name__=0   ,  __magic_name__=0				):
       super().__init__()
       lowerCamelCase		:     str          =				vocab_size
       lowerCamelCase		:     Optional[int]          =				bos_token_id





class 							A__		(     __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase    :   str   	=      """fsmt"""
      _UpperCAmelCase    :   List[Any]   	=      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
      def __init__(				self   ,  __magic_name__=["en", "de"]   ,  __magic_name__=4_2_0_2_4   ,  __magic_name__=4_2_0_2_4   ,  __magic_name__="relu"   ,  __magic_name__=1_0_2_4   ,  __magic_name__=2_0_0   ,  __magic_name__=1_0_2_4   ,  __magic_name__=4_0_9_6   ,  __magic_name__=1_2   ,  __magic_name__=1_6   ,  __magic_name__=0.0   ,  __magic_name__=4_0_9_6   ,  __magic_name__=1_2   ,  __magic_name__=1_6   ,  __magic_name__=0.0   ,  __magic_name__=0.0   ,  __magic_name__=0.1   ,  __magic_name__=0.0   ,  __magic_name__=0.02   ,  __magic_name__=2   ,  __magic_name__=True   ,  __magic_name__=True   ,  __magic_name__=False   ,  __magic_name__=5   ,  __magic_name__=1.0   ,  __magic_name__=False   ,  __magic_name__=True   ,  __magic_name__=1   ,  __magic_name__=0   ,  __magic_name__=2   ,  __magic_name__=2   ,  **__magic_name__   ,  ):
       lowerCamelCase		:     List[str]          =				langs
       lowerCamelCase		:     Dict          =				src_vocab_size
       lowerCamelCase		:     int          =				tgt_vocab_size
       lowerCamelCase		:     List[str]          =				d_model  # encoder_embed_dim and decoder_embed_dim

       lowerCamelCase		:     Dict          =				encoder_ffn_dim
       lowerCamelCase		:     List[Any]          =				encoder_layers
       lowerCamelCase		:     Any          =				encoder_attention_heads
       lowerCamelCase		:     List[str]          =				encoder_layerdrop
       lowerCamelCase		:     List[Any]          =				decoder_layerdrop
       lowerCamelCase		:     Tuple          =				decoder_ffn_dim
       lowerCamelCase		:     Tuple          =				decoder_layers
       lowerCamelCase		:     Dict          =				decoder_attention_heads
       lowerCamelCase		:     int          =				max_position_embeddings
       lowerCamelCase		:     Tuple          =				init_std  # Normal(0, this parameter)
       lowerCamelCase		:     Tuple          =				activation_function

       lowerCamelCase		:     Tuple          =				DecoderConfig(vocab_size=__magic_name__   ,  bos_token_id=__magic_name__				)
       if "decoder" in common_kwargs:
        del common_kwargs["decoder"]

       lowerCamelCase		:     Tuple          =				scale_embedding  # scale factor will be sqrt(d_model) if True

       # 3 Types of Dropout
       lowerCamelCase		:     Optional[int]          =				attention_dropout
       lowerCamelCase		:     Any          =				activation_dropout
       lowerCamelCase		:     Optional[int]          =				dropout

       lowerCamelCase		:     str          =				use_cache
       super().__init__(
           pad_token_id=__magic_name__   ,  bos_token_id=__magic_name__   ,  eos_token_id=__magic_name__   ,  decoder_start_token_id=__magic_name__   ,  is_encoder_decoder=__magic_name__   ,  tie_word_embeddings=__magic_name__   ,  forced_eos_token_id=__magic_name__   ,  max_length=__magic_name__   ,  num_beams=__magic_name__   ,  length_penalty=__magic_name__   ,  early_stopping=__magic_name__   ,  **__magic_name__   ,  )






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				copy.deepcopy(self.__dict__				)
       lowerCamelCase		:     Optional[int]          =				self.decoder.to_dict()
       lowerCamelCase		:     str          =				self.__class__.model_type
       return output






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def    _a (						lowerCamelCase							):
 lowerCamelCase		:     Dict          =				args.serialization_dir
 lowerCamelCase		:     List[Any]          =				args.pruning_method
 lowerCamelCase		:     List[Any]          =				args.threshold

 lowerCamelCase		:     str          =				torch.load(os.path.join(lowerCamelCase,     """pytorch_model.bin"""							),     map_location="""cpu"""							)

 lowerCamelCase		:     Optional[int]          =				0  # Number of remaining (not pruned) params in the encoder
 lowerCamelCase		:     Dict          =				0  # Number of params in the encoder

 print("""name""".ljust(60,     """ """							),     """Remaining Weights %""",     """Remaining Weight"""							)
 for name, param in st.items():
  if "encoder" not in name:
   continue

  if "mask_scores" in name:
   if pruning_method == "topK":
    lowerCamelCase		:     List[str]          =				TopKBinarizer.apply(lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "sigmoied_threshold":
    lowerCamelCase		:     Optional[int]          =				ThresholdBinarizer.apply(lowerCamelCase,     lowerCamelCase,     lowerCamelCase							).sum().item()
   elif pruning_method == "l0":
    lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				-0.1, 1.1
    lowerCamelCase		:     Any          =				torch.sigmoid(lowerCamelCase							)
    lowerCamelCase		:     str          =				s * (r - l) + l
    lowerCamelCase		:     str          =				s_bar.clamp(min=0.0,     max=1.0							)
    lowerCamelCase		:     Union[str, Any]          =				(mask > 0.0).sum().item()
   else:
    raise ValueError("""Unknown pruning method"""							)
   remaining_count += mask_ones
   print(name.ljust(60,     """ """							),     str(round(100 * mask_ones / param.numel(),     3							)							).ljust(20,     """ """							),     str(lowerCamelCase							)							)
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""""""							)
 print("""Remaining Weights (global) %: """,     100 * remaining_count / encoder_count							)


if __name__ == "__main__":
      _lowerCamelCase        =argparse.ArgumentParser()

      parser.add_argument(
          """--pruning_method""",
          choices=["""l0""", """topK""", """sigmoied_threshold"""],
          type=str,
          required=True,
          help=(
              """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
              """ pruning)"""
          ),
      )
      parser.add_argument(
          """--threshold""",
          type=float,
          required=False,
          help=(
              """For `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(
          """--serialization_dir""",
          type=str,
          required=True,
          help="""Folder containing the model that was previously fine-pruned""",
      )

      _lowerCamelCase        =parser.parse_args()

      main(args)




