import argparse
import os
import shutil

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


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     args.pruning_method
      SCREAMING_SNAKE_CASE_      =     args.threshold

      SCREAMING_SNAKE_CASE_      =     args.model_name_or_path.rstrip("/" )
      SCREAMING_SNAKE_CASE_      =     args.target_model_path

      print(F'''Load fine-pruned model from {model_name_or_path}''' )
      SCREAMING_SNAKE_CASE_      =     torch.load(os.path.join(__UpperCamelCase   ,					"pytorch_model.bin" ) )
      SCREAMING_SNAKE_CASE_      =     {}

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

      if target_model_path is None:
            SCREAMING_SNAKE_CASE_      =     os.path.join(
                os.path.dirname(__UpperCamelCase )   ,					F'''bertarized_{os.path.basename(__UpperCamelCase )}''' )

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

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


if __name__ == "__main__":
      A   :     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",
      )

      A   :     Tuple       =				parser.parse_args()

      main(args)



def    a__       (						__UpperCamelCase ):
      if not head:
            return True
      # split the list to two parts
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     head.next, head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_      =     fast.next.next
            SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     None  # Don't forget here! But forget still works!
      # reverse the second part
      SCREAMING_SNAKE_CASE_      =     None
      while second:
            SCREAMING_SNAKE_CASE_      =     second.next
            SCREAMING_SNAKE_CASE_      =     node
            SCREAMING_SNAKE_CASE_      =     second
            SCREAMING_SNAKE_CASE_      =     nxt
      # compare two parts
      # second part has the same or one less node
      while node:
            if node.val != head.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     node.next
            SCREAMING_SNAKE_CASE_      =     head.next
      return True


def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True

      # 1. Get the midpoint (slow)
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     fast.next.next, slow.next

      # 2. Push the second half into the stack
      SCREAMING_SNAKE_CASE_      =     [slow.val]
      while slow.next:
            SCREAMING_SNAKE_CASE_      =     slow.next
            stack.append(slow.val )

      # 3. Comparison
      while stack:
            if stack.pop() != cur.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     cur.next

      return True





def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True
      SCREAMING_SNAKE_CASE_      =     {}
      SCREAMING_SNAKE_CASE_      =     0
      while head:
            if head.val in d:
                  d[head.val].append(__UpperCamelCase )
            else:
                  SCREAMING_SNAKE_CASE_      =     [pos]
            SCREAMING_SNAKE_CASE_      =     head.next
            pos += 1
      SCREAMING_SNAKE_CASE_      =     pos - 1
      SCREAMING_SNAKE_CASE_      =     0
      for v in d.values():
            if len(__UpperCamelCase ) % 2 != 0:
                  middle += 1
            else:
                  SCREAMING_SNAKE_CASE_      =     0
                  for i in range(0   ,					len(__UpperCamelCase ) ):
                        if v[i] + v[len(__UpperCamelCase ) - 1 - step] != checksum:
                              return False
                        step += 1
            if middle > 1:
                  return False
      return True


