




"""simple docstring"""



import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						int:
 A__			=							args.serialization_dir
 A__			=							args.pruning_method
 A__			=							args.threshold

 A__			=							torch.load(os.path.join(lowercase_     ,					"pytorch_model.bin"		)     ,					map_location="cpu"		)

 A__			=							0  # Number of remaining (not pruned) params in the encoder
 A__			=							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":
    A__			=							TopKBinarizer.apply(lowercase_     ,					lowercase_		).sum().item()
   elif pruning_method == "sigmoied_threshold":
    A__			=							ThresholdBinarizer.apply(lowercase_     ,					lowercase_     ,					lowercase_		).sum().item()
   elif pruning_method == "l0":
    A__, A__			=							-0.1, 1.1
    A__			=							torch.sigmoid(lowercase_		)
    A__			=							s * (r - l) + l
    A__			=							s_bar.clamp(min=0.0     ,					max=1.0		)
    A__			=							(mask > 0.0).sum().item()
   else:
    raise ValueError("Unknown pruning method"		)
   remaining_count += mask_ones
   print(name.ljust(60     ,					" "		)     ,					str(round(1_00 * mask_ones / param.numel()     ,					3		)		).ljust(20     ,					" "		)     ,					str(lowercase_		)		)
  else:
   encoder_count += param.numel()
   if "bias" in name or "LayerNorm" in name:
    remaining_count += param.numel()

 print(""		)
 print("Remaining Weights (global) %: "     ,					1_00 * remaining_count / encoder_count		)


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

      SCREAMING_SNAKE_CASE							=			parser.parse_args()

      main(args)











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							=			{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPRContextEncoderTokenizerFast",
          "DPRQuestionEncoderTokenizerFast",
          "DPRReaderTokenizerFast",
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPRContextEncoder",
          "DPRPretrainedContextEncoder",
          "DPRPreTrainedModel",
          "DPRPretrainedQuestionEncoder",
          "DPRPretrainedReader",
          "DPRQuestionEncoder",
          "DPRReader",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDPRContextEncoder",
          "TFDPRPretrainedContextEncoder",
          "TFDPRPretrainedQuestionEncoder",
          "TFDPRPretrainedReader",
          "TFDPRQuestionEncoder",
          "TFDPRReader",
      ]


if TYPE_CHECKING:
      from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
      from .tokenization_dpr import (
          DPRContextEncoderTokenizer,
          DPRQuestionEncoderTokenizer,
          DPRReaderOutput,
          DPRReaderTokenizer,
      )

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_dpr_fast import (
                DPRContextEncoderTokenizerFast,
                DPRQuestionEncoderTokenizerFast,
                DPRReaderTokenizerFast,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpr import (
                DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPRContextEncoder,
                DPRPretrainedContextEncoder,
                DPRPreTrainedModel,
                DPRPretrainedQuestionEncoder,
                DPRPretrainedReader,
                DPRQuestionEncoder,
                DPRReader,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_dpr import (
                TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDPRContextEncoder,
                TFDPRPretrainedContextEncoder,
                TFDPRPretrainedQuestionEncoder,
                TFDPRPretrainedReader,
                TFDPRQuestionEncoder,
                TFDPRReader,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE							=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





