

import argparse
import os
import shutil

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

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       args.pruning_method
	SCREAMING_SNAKE_CASE   :      List[str]				     =       args.threshold

	SCREAMING_SNAKE_CASE   :      Any				     =       args.model_name_or_path.rstrip("/")
	SCREAMING_SNAKE_CASE   :      Any				     =       args.target_model_path

	print(f"Load fine-pruned model from {model_name_or_path}")
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.load(os.path.join(_a							,			"pytorch_model.bin"))
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {}

	for name, tensor in model.items():
		if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
			SCREAMING_SNAKE_CASE   :      str				     =       tensor
			print(f"Copied layer {name}")
		elif "classifier" in name or "qa_output" in name:
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tensor
			print(f"Copied layer {name}")
		elif "bias" in name:
			SCREAMING_SNAKE_CASE   :      List[str]				     =       tensor
			print(f"Copied layer {name}")
		else:
			if pruning_method == "magnitude":
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       MagnitudeBinarizer.apply(inputs=_a							,			threshold=_a)
				SCREAMING_SNAKE_CASE   :      Any				     =       tensor * mask
				print(f"Pruned layer {name}")
			elif pruning_method == "topK":
				if "mask_scores" in name:
					continue
				SCREAMING_SNAKE_CASE   :      Dict				     =       name[:-6]
				SCREAMING_SNAKE_CASE   :      int				     =       model[f"{prefix_}mask_scores"]
				SCREAMING_SNAKE_CASE   :      Tuple				     =       TopKBinarizer.apply(_a							,			_a)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       tensor * mask
				print(f"Pruned layer {name}")
			elif pruning_method == "sigmoied_threshold":
				if "mask_scores" in name:
					continue
				SCREAMING_SNAKE_CASE   :      List[str]				     =       name[:-6]
				SCREAMING_SNAKE_CASE   :      Any				     =       model[f"{prefix_}mask_scores"]
				SCREAMING_SNAKE_CASE   :      int				     =       ThresholdBinarizer.apply(_a							,			_a							,			_a)
				SCREAMING_SNAKE_CASE   :      int				     =       tensor * mask
				print(f"Pruned layer {name}")
			elif pruning_method == "l0":
				if "mask_scores" in name:
					continue
				SCREAMING_SNAKE_CASE   :      Tuple				     =       name[:-6]
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model[f"{prefix_}mask_scores"]
				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       -0.1, 1.1
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.sigmoid(_a)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       s * (r - l) + l
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       s_bar.clamp(min=0.0							,			max=1.0)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tensor * mask
				print(f"Pruned layer {name}")
			else:
				raise ValueError("Unknown pruning method")

	if target_model_path is None:
		SCREAMING_SNAKE_CASE   :      Dict				     =       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__":
			a_						     =    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_						     =    parser.parse_args()

			main(args)


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


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






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           =None






class _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =ParquetConfig
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: List[Any]	)							->       Tuple:

				"""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}"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      str				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [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:
								SCREAMING_SNAKE_CASE   :      Dict				     =       datasets.Features.from_arrow_schema(pq.read_schema(a	)	)
							break
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Dict      ,      a							: pa.Table	)							->       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
					SCREAMING_SNAKE_CASE   :      str				     =       table_cast(a      ,      self.info.features.arrow_schema	)
				return pa_table






			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       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:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       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	)	):
								SCREAMING_SNAKE_CASE   :      int				     =       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