



import argparse
import os
import shutil

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





def 		a__			(		_UpperCamelCase			: Any							):
   __lowerCamelCase													=    args.pruning_method
   __lowerCamelCase													=    args.threshold

   __lowerCamelCase													=    args.model_name_or_path.rstrip('''/'''							)
   __lowerCamelCase													=    args.target_model_path

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

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

   if target_model_path is None:
      __lowerCamelCase													=    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_			=					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_			=					parser.parse_args()

  main(args)











import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed



class  __lowerCAmelCase     (	lowerCAmelCase__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=3.6				):



    '''simple docstring'''
    __lowerCamelCase													=    tokenizer
    __lowerCamelCase													=    tokenizer.bos_token_id
    __lowerCamelCase													=    dataset
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    seq_length * chars_per_token * num_of_sequences




 def __iter__(						self				):



    '''simple docstring'''
    __lowerCamelCase													=    iter(self.dataset				)
    __lowerCamelCase													=    True
    while more_examples:
       __lowerCamelCase							,__lowerCamelCase													=    [], 0
       while True:
          if buffer_len >= self.input_characters:
             break
          try:
             buffer.append(next(__UpperCAmelCase				)['''content''']				)
             buffer_len += len(buffer[-1]				)
          except StopIteration:
             __lowerCamelCase													=    False
             break
       __lowerCamelCase													=    tokenizer(__UpperCAmelCase	,				truncation=__UpperCAmelCase				)['''input_ids''']
       __lowerCamelCase													=    []
       for tokenized_input in tokenized_inputs:
          all_token_ids.extend(tokenized_input + [self.concat_token_id]				)
       for i in range(0	,				len(__UpperCAmelCase				)	,				self.seq_length				):
          __lowerCamelCase													=    all_token_ids[i : i + self.seq_length]
          if len(__UpperCAmelCase				) == self.seq_length:
             yield torch.tensor(__UpperCAmelCase				)





def 		a__			(		_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    {'''streaming''': True}
   __lowerCamelCase													=    load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase							)
   __lowerCamelCase													=    ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=args.batch_size							)
   return eval_dataloader





def 		a__			(		_UpperCamelCase			: str							):
   model.eval()
   __lowerCamelCase													=    []
   for step, batch in enumerate(_UpperCamelCase							):
      with torch.no_grad():
         __lowerCamelCase													=    model(_UpperCamelCase ,labels=_UpperCamelCase							)
      __lowerCamelCase													=    outputs.loss.repeat(args.batch_size							)
      losses.append(accelerator.gather(_UpperCamelCase							)							)

      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   __lowerCamelCase													=    torch.mean(torch.cat(_UpperCamelCase							)							)
   try:
      __lowerCamelCase													=    torch.exp(_UpperCamelCase							)
   except OverflowError:
      __lowerCamelCase													=    float('''inf'''							)
   return loss.item(), perplexity.item()


# Setup Accelerator
a_			=					Accelerator()

# Parse configuration
a_			=					HfArgumentParser(EvaluationArguments)
a_			=					parser.parse_args()
set_seed(args.seed)

# Logging
a_			=					logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
a_			=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_			=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a_			=					create_dataloader(args)

# Prepare everything with our `accelerator`.
a_		,						a_			=					accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
a_		,						a_			=					evaluate(args)





logger.info(f"loss/eval: {eval_loss}, perplexity: {perplexity}")






