



"""simple docstring"""







import argparse
import os
import shutil

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

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Tuple						)						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Union[str, Any]								= args.pruning_method
  lowerCAmelCase_   :				Dict								= args.threshold

  lowerCAmelCase_   :				List[Any]								= args.model_name_or_path.rstrip('/'						)
  lowerCAmelCase_   :				Optional[Any]								= args.target_model_path

  print(f"Load fine-pruned model from {model_name_or_path}"						)
  lowerCAmelCase_   :				Optional[Any]								= torch.load(os.path.join(lowerCAmelCase__    ,   '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_   :				Any								= tensor
      print(f"Copied layer {name}"						)
    elif "classifier" in name or "qa_output" in name:
      lowerCAmelCase_   :				Tuple								= 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_   :				int								= MagnitudeBinarizer.apply(inputs=lowerCAmelCase__    ,   threshold=lowerCAmelCase__						)
        lowerCAmelCase_   :				Dict								= tensor * mask
        print(f"Pruned layer {name}"						)
      elif pruning_method == "topK":
        if "mask_scores" in name:
          continue
        lowerCAmelCase_   :				Tuple								= name[:-6]
        lowerCAmelCase_   :				Tuple								= model[f"{prefix_}mask_scores"]
        lowerCAmelCase_   :				int								= TopKBinarizer.apply(lowerCAmelCase__    ,   lowerCAmelCase__						)
        lowerCAmelCase_   :				str								= tensor * mask
        print(f"Pruned layer {name}"						)
      elif pruning_method == "sigmoied_threshold":
        if "mask_scores" in name:
          continue
        lowerCAmelCase_   :				List[str]								= name[:-6]
        lowerCAmelCase_   :				List[str]								= model[f"{prefix_}mask_scores"]
        lowerCAmelCase_   :				List[str]								= ThresholdBinarizer.apply(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
        lowerCAmelCase_   :				str								= tensor * mask
        print(f"Pruned layer {name}"						)
      elif pruning_method == "l0":
        if "mask_scores" in name:
          continue
        lowerCAmelCase_   :				Optional[Any]								= name[:-6]
        lowerCAmelCase_   :				str								= model[f"{prefix_}mask_scores"]
        lowerCAmelCase_						,lowerCAmelCase_   :				Optional[Any]								= -0.1, 1.1
        lowerCAmelCase_   :				Dict								= torch.sigmoid(lowerCAmelCase__						)
        lowerCAmelCase_   :				Optional[int]								= s * (r - l) + l
        lowerCAmelCase_   :				str								= s_bar.clamp(min=0.0    ,   max=1.0						)
        lowerCAmelCase_   :				int								= tensor * mask
        print(f"Pruned layer {name}"						)
      else:
        raise ValueError('Unknown pruning method'						)

  if target_model_path is None:
    lowerCAmelCase_   :				Optional[int]								= os.path.join(
        os.path.dirname(lowerCAmelCase__						)    ,   f"bertarized_{os.path.basename(lowerCAmelCase__						)}"						)

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

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


if __name__ == "__main__":
     lowercase__		:				List[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""",
     )

     lowercase__		:				str          =	parser.parse_args()

     main(args)










"""simple docstring"""







from typing import List, Optional, Union

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


lowercase__		:				Union[str, Any]          =	logging.get_logger(__name__)

lowercase__		:				List[str]          =	{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """informer"""
      _SCREAMING_SNAKE_CASE  				=     {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
          """num_hidden_layers""": """encoder_layers""",
      }



      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      str = "student_t"    ,			SCREAMING_SNAKE_CASE_				:      str = "nll"    ,			SCREAMING_SNAKE_CASE_				:      int = 1    ,			SCREAMING_SNAKE_CASE_				:      List[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, bool]] = "mean"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None    ,			SCREAMING_SNAKE_CASE_				:      int = 6_4    ,			SCREAMING_SNAKE_CASE_				:      int = 3_2    ,			SCREAMING_SNAKE_CASE_				:      int = 3_2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      str = "gelu"    ,			SCREAMING_SNAKE_CASE_				:      float = 0.05    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      int = 1_0_0    ,			SCREAMING_SNAKE_CASE_				:      float = 0.02    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      str = "prob"    ,			SCREAMING_SNAKE_CASE_				:      int = 5    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			**SCREAMING_SNAKE_CASE_				:      int    ,			):
        # time series specific configuration
        lowerCAmelCase_   :				Dict								= prediction_length
        lowerCAmelCase_   :				List[str]								= context_length or prediction_length
        lowerCAmelCase_   :				List[Any]								= distribution_output
        lowerCAmelCase_   :				int								= loss
        lowerCAmelCase_   :				Optional[int]								= input_size
        lowerCAmelCase_   :				Tuple								= num_time_features
        lowerCAmelCase_   :				List[str]								= lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
        lowerCAmelCase_   :				int								= scaling
        lowerCAmelCase_   :				List[Any]								= num_dynamic_real_features
        lowerCAmelCase_   :				Union[str, Any]								= num_static_real_features
        lowerCAmelCase_   :				Optional[int]								= num_static_categorical_features

        # set cardinality
        if cardinality and num_static_categorical_features > 0:
          if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
            raise ValueError(
                'The cardinality should be a list of the same length as `num_static_categorical_features`'       )
          lowerCAmelCase_   :				str								= cardinality
        else:
          lowerCAmelCase_   :				Any								= [0]

        # set embedding_dimension
        if embedding_dimension and num_static_categorical_features > 0:
          if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
            raise ValueError(
                'The embedding dimension should be a list of the same length as `num_static_categorical_features`'       )
          lowerCAmelCase_   :				Optional[int]								= embedding_dimension
        else:
          lowerCAmelCase_   :				Union[str, Any]								= [min(5_0    ,			(cat + 1) // 2       ) for cat in self.cardinality]

        lowerCAmelCase_   :				Optional[int]								= num_parallel_samples

        # Transformer architecture configuration
        lowerCAmelCase_   :				Any								= input_size * len(self.lags_sequence       ) + self._number_of_features
        lowerCAmelCase_   :				Any								= d_model
        lowerCAmelCase_   :				Union[str, Any]								= encoder_attention_heads
        lowerCAmelCase_   :				Optional[Any]								= decoder_attention_heads
        lowerCAmelCase_   :				Any								= encoder_ffn_dim
        lowerCAmelCase_   :				List[str]								= decoder_ffn_dim
        lowerCAmelCase_   :				Optional[Any]								= encoder_layers
        lowerCAmelCase_   :				Tuple								= decoder_layers

        lowerCAmelCase_   :				Optional[int]								= dropout
        lowerCAmelCase_   :				Dict								= attention_dropout
        lowerCAmelCase_   :				int								= activation_dropout
        lowerCAmelCase_   :				Dict								= encoder_layerdrop
        lowerCAmelCase_   :				str								= decoder_layerdrop

        lowerCAmelCase_   :				Union[str, Any]								= activation_function
        lowerCAmelCase_   :				Union[str, Any]								= init_std

        lowerCAmelCase_   :				Union[str, Any]								= use_cache

        # Informer
        lowerCAmelCase_   :				Optional[int]								= attention_type
        lowerCAmelCase_   :				Any								= sampling_factor
        lowerCAmelCase_   :				int								= distil

        super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )





      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return (
            sum(self.embedding_dimension       )
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )





