





import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


snake_case_				=			datasets.utils.logging.get_logger(__name__)

@dataclass
class 		SCREAMING_SNAKE_CASE__				(       datasets.BuilderConfig  ):
      A_						:				int							=    10_000
      A_						:				Optional[List[str]]							=    None
      A_						:				Optional[datasets.Features]							=    None






class 		SCREAMING_SNAKE_CASE__				(       datasets.ArrowBasedBuilder  ):
      A_						:				Optional[Any]							=    ParquetConfig


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         return datasets.DatasetInfo(features=self.config.features      )


      def    a (self       :      List[str]				,			a__       :      List[str]      ):





         """simple docstring"""



         if not self.config.data_files:
            raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""      )
         __snake_case							       =   dl_manager.download_and_extract(self.config.data_files      )
         if isinstance(a__				,			(str, list, tuple)      ):
            __snake_case							       =   data_files
            if isinstance(a__				,			a__      ):
               __snake_case							       =   [files]
            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
            __snake_case							       =   [dl_manager.iter_files(a__      ) for file in files]
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,			gen_kwargs={'''files''': files}      )]
         __snake_case							       =   []
         for split_name, files in data_files.items():
            if isinstance(a__				,			a__      ):
               __snake_case							       =   [files]
            # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
            __snake_case							       =   [dl_manager.iter_files(a__      ) for file in files]
            # Infer features is they are stoed in the arrow schema
            if self.info.features is None:
               for file in itertools.chain.from_iterable(a__      ):
                  with open(a__				,			'''rb'''      ) as f:
                     __snake_case							       =   datasets.Features.from_arrow_schema(pq.read_schema(a__      )      )
                  break
            splits.append(datasets.SplitGenerator(name=a__				,			gen_kwargs={'''files''': files}      )      )
         return splits


      def    a (self       :      str				,			a__       :      pa.Table      ):





         """simple docstring"""



         if self.info.features is not None:
            # more expensive cast to support nested features with keys in a different order
            # allows str <-> int/float or str to Audio for example
            __snake_case							       =   table_cast(a__				,			self.info.features.arrow_schema      )
         return pa_table


      def    a (self       :      Any				,			a__       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   self.info.features.arrow_schema if self.info.features is not None else None
         if self.info.features is not None and self.config.columns is not None:
            if sorted(field.name for field in schema      ) != sorted(self.config.columns      ):
               raise ValueError(
                   f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""      )
         for file_idx, file in enumerate(itertools.chain.from_iterable(a__      )      ):
            with open(a__				,			'''rb'''      ) as f:
               __snake_case							       =   pq.ParquetFile(a__      )
               try:
                  for batch_idx, record_batch in enumerate(
                      parquet_file.iter_batches(batch_size=self.config.batch_size				,			columns=self.config.columns      )      ):
                     __snake_case							       =   pa.Table.from_batches([record_batch]      )
                     # Uncomment for debugging (will print the Arrow table size and elements)
                     # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                     # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                     yield f"""{file_idx}_{batch_idx}""", self._cast_table(a__      )
               except ValueError as e:
                  logger.error(f"""Failed to read file '{file}' with error {type(a__      )}: {e}"""      )
                  raise







import argparse
import os
import shutil

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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       A__       =    args.pruning_method
       A__       =    args.threshold

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

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

       for name, tensor in model.items():
              if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "classifier" in name or "qa_output" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              elif "bias" in name:
                     A__       =    tensor
                     print(f"""Copied layer {name}"""						)
              else:
                     if pruning_method == "magnitude":
                            A__       =    MagnitudeBinarizer.apply(inputs=lowercase_ ,				threshold=lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "topK":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    TopKBinarizer.apply(lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "sigmoied_threshold":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            A__       =    ThresholdBinarizer.apply(lowercase_ ,				lowercase_ ,				lowercase_						)
                            A__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     elif pruning_method == "l0":
                            if "mask_scores" in name:
                                   continue
                            A__       =    name[:-6]
                            A__       =    model[f"""{prefix_}mask_scores"""]
                            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__       =    tensor * mask
                            print(f"""Pruned layer {name}"""						)
                     else:
                            raise ValueError('''Unknown pruning method'''						)

       if target_model_path is None:
              A__       =    os.path.join(
                  os.path.dirname(lowercase_						) ,				f"""bertarized_{os.path.basename(lowercase_						)}"""						)

       if not os.path.isdir(lowercase_						):
              shutil.copytree(lowercase_ ,				lowercase_						)
              print(f"""\nCreated folder {target_model_path}"""						)

       torch.save(lowercase_ ,				os.path.join(lowercase_ ,				'''pytorch_model.bin'''						)						)
       print('''\nPruned model saved! See you later!'''						)


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[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""",
  )

  _lowerCamelCase	:		int							       =   parser.parse_args()

  main(args)


