


'''simple docstring'''




import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer


def    UpperCAmelCase_  (       A		,	A		,	A		,	A		,	A=True		,	A="pt"      ):
   '''simple docstring'''





   _a					:  List[str]						=  {'add_prefix_space': True} if isinstance(A		,	A      ) and not line.startswith(' '      ) else {}
   _a					:  int						=  padding_side
   return tokenizer(
       [line]		,	max_length=A		,	padding='max_length' if pad_to_max_length else None		,	truncation=A		,	return_tensors=A		,	add_special_tokens=A		,	**A		,	)


def    UpperCAmelCase_  (       A		,	A		,	A=None		,	):
   '''simple docstring'''





   _a					:  List[Any]						=  input_ids.ne(A      ).any(dim=0      )
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class 					a			(				snake_case__  ):
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_="train"		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=None		,      lowerCamelCase_=""		,      )      ->    Optional[int]:
    super().__init__()
    _a					:  int						=  Path(lowerCamelCase_      ).joinpath(type_path + '.source'      )
    _a					:  List[Any]						=  Path(lowerCamelCase_      ).joinpath(type_path + '.target'      )
    _a					:  Tuple						=  self.get_char_lens(self.src_file      )
    _a					:  Dict						=  max_source_length
    _a					:  Any						=  max_target_length
    assert min(self.src_lens      ) > 0, F'''found empty line in {self.src_file}'''
    _a					:  Tuple						=  tokenizer
    _a					:  int						=  prefix
    if n_obs is not None:
       _a					:  Any						=  self.src_lens[:n_obs]
    _a					:  List[str]						=  src_lang
    _a					:  Any						=  tgt_lang




 def __len__(    self      )      ->    Tuple:
    return len(self.src_lens      )




 def __getitem__(    self		,      lowerCamelCase_      )      ->    Dict[str, torch.Tensor]:
    _a					:  Optional[Any]						=  index + 1  # linecache starts at 1
    _a					:  Union[str, Any]						=  self.prefix + linecache.getline(str(self.src_file      )		,      lowerCamelCase_      ).rstrip('\n'      )
    _a					:  Optional[int]						=  linecache.getline(str(self.tgt_file      )		,      lowerCamelCase_      ).rstrip('\n'      )
    assert source_line, F'''empty source line for index {index}'''
    assert tgt_line, F'''empty tgt line for index {index}'''

    # Need to add eos token manually for T5
    if isinstance(self.tokenizer		,      lowerCamelCase_      ):
       source_line += self.tokenizer.eos_token
       tgt_line += self.tokenizer.eos_token

    # Pad source and target to the right
    _a					:  str						=  (
        self.tokenizer.question_encoder if isinstance(self.tokenizer		,      lowerCamelCase_      ) else self.tokenizer
    )
    _a					:  Dict						=  self.tokenizer.generator if isinstance(self.tokenizer		,      lowerCamelCase_      ) else self.tokenizer

    _a					:  Optional[int]						=  encode_line(lowerCamelCase_		,      lowerCamelCase_		,      self.max_source_length		,      'right'      )
    _a					:  Union[str, Any]						=  encode_line(lowerCamelCase_		,      lowerCamelCase_		,      self.max_target_length		,      'right'      )

    _a					:  List[str]						=  source_inputs['input_ids'].squeeze()
    _a					:  Union[str, Any]						=  target_inputs['input_ids'].squeeze()
    _a					:  Optional[Any]						=  source_inputs['attention_mask'].squeeze()
    return {
        "input_ids": source_ids,
        "attention_mask": src_mask,
        "decoder_input_ids": target_ids,
    }




 @staticmethod
 def   __UpperCamelCase   (    lowerCamelCase_      )      ->    Union[str, Any]:
    return [len(lowerCamelCase_      ) for x in Path(lowerCamelCase_      ).open().readlines()]




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    Dict[str, torch.Tensor]:
    _a					:  Any						=  torch.stack([x['input_ids'] for x in batch]      )
    _a					:  int						=  torch.stack([x['attention_mask'] for x in batch]      )
    _a					:  Dict						=  torch.stack([x['decoder_input_ids'] for x in batch]      )
    _a					:  List[Any]						=  (
        self.tokenizer.generator.pad_token_id
        if isinstance(self.tokenizer		,      lowerCamelCase_      )
        else self.tokenizer.pad_token_id
    )
    _a					:  List[Any]						=  (
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer		,      lowerCamelCase_      )
        else self.tokenizer.pad_token_id
    )
    _a					:  Tuple						=  trim_batch(lowerCamelCase_		,      lowerCamelCase_      )
    _a		,						_a					:  Optional[int]						=  trim_batch(lowerCamelCase_		,      lowerCamelCase_		,      attention_mask=lowerCamelCase_      )
    _a					:  Any						=  {
        'input_ids': source_ids,
        'attention_mask': source_mask,
        'decoder_input_ids': y,
    }
    return batch





UpperCAmelCase_      :		Any          =		getLogger(__name__)


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





   return list(itertools.chain.from_iterable(A      )      )


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





   _a					:  Tuple						=  get_git_info()
   save_json(A		,	os.path.join(A		,	'git_log.json'      )      )


def    UpperCAmelCase_  (       A		,	A		,	A=4		,	**A      ):
   '''simple docstring'''





   with open(A		,	'w'      ) as f:
      json.dump(A		,	A		,	indent=A		,	**A      )


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





   with open(A      ) as f:
      return json.load(A      )


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





   _a					:  Any						=  git.Repo(search_parent_directories=A      )
   _a					:  Optional[Any]						=  {
       'repo_id': str(A      ),
       'repo_sha': str(repo.head.object.hexsha      ),
       'repo_branch': str(repo.active_branch      ),
       'hostname': str(socket.gethostname()      ),
   }
   return repo_infos


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





   return list(map(A		,	A      )      )


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





   with open(A		,	'wb'      ) as f:
      return pickle.dump(A		,	A      )


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





   def remove_articles(A      ):
      return re.sub(r'\b(a|an|the)\b'		,	' '		,	A      )

   def white_space_fix(A      ):
      return " ".join(text.split()      )

   def remove_punc(A      ):
      _a					:  Any						=  set(string.punctuation      )
      return "".join(ch for ch in text if ch not in exclude      )

   def lower(A      ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(A      )      )      )      )


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





   _a					:  Optional[int]						=  normalize_answer(A      ).split()
   _a					:  str						=  normalize_answer(A      ).split()
   _a					:  Optional[Any]						=  Counter(A      ) & Counter(A      )
   _a					:  List[str]						=  sum(common.values()      )
   if num_same == 0:
      return 0
   _a					:  List[str]						=  1.0 * num_same / len(A      )
   _a					:  List[str]						=  1.0 * num_same / len(A      )
   _a					:  List[str]						=  (2 * precision * recall) / (precision + recall)
   return fa


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





   return normalize_answer(A      ) == normalize_answer(A      )


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





   assert len(A      ) == len(A      )
   _a					:  Optional[Any]						=  0
   for hypo, pred in zip(A		,	A      ):
      em += exact_match_score(A		,	A      )
   if len(A      ) > 0:
      em /= len(A      )
   return {"em": em}


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





   return model_prefix.startswith('rag'      )







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





   _a					:  Tuple						=  {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   _a					:  List[Any]						=  'dropout_rate'
   for p in extra_params:
      if getattr(A		,	A		,	A      ):
         if not hasattr(A		,	A      ) and not hasattr(A		,	equivalent_param[p]      ):
            logger.info('config doesn\'t have a `{}` attribute'.format(A      )      )
            delattr(A		,	A      )
            continue
         _a					:  Tuple						=  p if hasattr(A		,	A      ) else equivalent_param[p]
         setattr(A		,	A		,	getattr(A		,	A      )      )
         delattr(A		,	A      )
   return hparams, config






'''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)


