

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		str							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    args.serialization_dir
    SCREAMING_SNAKE_CASE_						=    args.pruning_method
    SCREAMING_SNAKE_CASE_						=    args.threshold

    SCREAMING_SNAKE_CASE_						=    torch.load(os.path.join(_SCREAMING_SNAKE_CASE			, 'pytorch_model.bin'							)			, map_location='cpu'							)

    SCREAMING_SNAKE_CASE_						=    0  # Number of remaining (not pruned) params in the encoder
    SCREAMING_SNAKE_CASE_						=    0  # Number of params in the encoder

    print('name'.ljust(60			, ' '							)			, 'Remaining Weights %'			, 'Remaining Weight'							)
    for name, param in st.items():
        if "encoder" not in name:
            continue

        if "mask_scores" in name:
            if pruning_method == "topK":
                SCREAMING_SNAKE_CASE_						=    TopKBinarizer.apply(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							).sum().item()
            elif pruning_method == "sigmoied_threshold":
                SCREAMING_SNAKE_CASE_						=    ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							).sum().item()
            elif pruning_method == "l0":
                SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_						=    -0.1, 1.1
                SCREAMING_SNAKE_CASE_						=    torch.sigmoid(_SCREAMING_SNAKE_CASE							)
                SCREAMING_SNAKE_CASE_						=    s * (r - l) + l
                SCREAMING_SNAKE_CASE_						=    s_bar.clamp(min=0.0			, max=1.0							)
                SCREAMING_SNAKE_CASE_						=    (mask > 0.0).sum().item()
            else:
                raise ValueError('Unknown pruning method'							)
            remaining_count += mask_ones
            print(name.ljust(60			, ' '							)			, str(round(100 * mask_ones / param.numel()			, 3							)							).ljust(20			, ' '							)			, str(_SCREAMING_SNAKE_CASE							)							)
        else:
            encoder_count += param.numel()
            if "bias" in name or "LayerNorm" in name:
                remaining_count += param.numel()

    print(''							)
    print('Remaining Weights (global) %: '			, 100 * remaining_count / encoder_count							)


if __name__ == "__main__":
       UpperCamelCase__	:   Dict								=     argparse.ArgumentParser()

       parser.add_argument(
           "--pruning_method",
           choices=["l0", "topK", "sigmoied_threshold"],
           type=str,
           required=True,
           help=(
               "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
               " pruning)"
           ),
       )
       parser.add_argument(
           "--threshold",
           type=float,
           required=False,
           help=(
               "For `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(
           "--serialization_dir",
           type=str,
           required=True,
           help="Folder containing the model that was previously fine-pruned",
       )

       UpperCamelCase__	:   Dict								=     parser.parse_args()

       main(args)





import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_reformer import ReformerTokenizer
else:
       UpperCamelCase__	:   Optional[int]								=     None


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


UpperCamelCase__	:   Any								=     "▁"

UpperCamelCase__	:   Any								=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__	:   List[Any]								=     {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

UpperCamelCase__	:   Optional[Any]								=     {
    "google/reformer-crime-and-punishment": 524_288,
}



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						int    			=	VOCAB_FILES_NAMES
  __lowerCAmelCase  :						int    			=	PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase  :						Tuple    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase  :						List[str]    			=	['input_ids', 'attention_mask']
  __lowerCAmelCase  :						Dict    			=	ReformerTokenizer

  def __init__(       self    ,		_A=None    ,		_A=None    ,		_A="</s>"    ,		_A="<unk>"    ,		_A=[]    ,		**_A    ,		):
      super().__init__(
          _A    ,		tokenizer_file=_A    ,		eos_token=_A    ,		unk_token=_A    ,		additional_special_tokens=_A    ,		**_A    ,		)

      SCREAMING_SNAKE_CASE_						=    vocab_file
      SCREAMING_SNAKE_CASE_						=    False if not self.vocab_file else True


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = None):
      if not self.can_save_slow_tokenizer:
          raise ValueError(
              'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
              'tokenizer.')

      if not os.path.isdir(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_						=    os.path.join(
          _A    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

      if os.path.abspath(self.vocab_file) != os.path.abspath(_A):
          copyfile(self.vocab_file    ,		_A)

      return (out_vocab_file,)


