


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


a_ :Optional[Any]  							=			logging.get_logger(__name__)

a_ :Optional[Any]  							=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a_ :Tuple  							=			{
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

a_ :Tuple  							=			{
    'facebook/blenderbot_small-90M': 5_12,
}

class 							lowercase  (						_UpperCAmelCase			):
     lowerCamelCase			:			str      = VOCAB_FILES_NAMES
     lowerCamelCase			:			int      = PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase			:			int      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase			:			int      = BlenderbotSmallTokenizer







     def __init__(						self :						List[Any]   ,   _lowercase :						Optional[int]=None   ,   _lowercase :						Union[str, Any]=None   ,   _lowercase :						List[Any]="<|endoftext|>"   ,   _lowercase :						List[Any]="<|endoftext|>"   ,   _lowercase :						Any="<|endoftext|>"   ,   _lowercase :						int=False   ,   _lowercase :						Optional[int]=True   ,   **_lowercase :						List[Any]   ,   ):
           super().__init__(
               ByteLevelBPETokenizer(
                   vocab=_lowercase   ,   merges=_lowercase   ,   add_prefix_space=_lowercase   ,   trim_offsets=_lowercase   ,   )   ,   bos_token=_lowercase   ,   eos_token=_lowercase   ,   unk_token=_lowercase   ,   **_lowercase   ,   )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		add_prefix_space







     def 							lowercase__       (						self :						Tuple   ,   _lowercase :						Optional[Any]   ,   _lowercase :						Optional[int]=None      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                 return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







     def 							lowercase__       (						self :						List[Any]   ,   _lowercase :						List[int]   ,   _lowercase :						Optional[List[int]] = None      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		[self.sep_token_id]
           SCREAMING_SNAKE_CASE__       :  Dict						     =		[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep      ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]









import argparse
import os
import shutil

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 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_     :    Union[str, 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_     :    Dict     		=   parser.parse_args()

      main(args)

