




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					args.serialization_dir
	snake_case_		   =					args.pruning_method
	snake_case_		   =					args.threshold

	snake_case_		   =					torch.load(os.path.join(_A					,     "pytorch_model.bin"							)					,     map_location="cpu"							)

	snake_case_		   =					0  # Number of remaining (not pruned) params in the encoder
	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":
				snake_case_		   =					TopKBinarizer.apply(_A					,     _A							).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case_		   =					ThresholdBinarizer.apply(_A					,     _A					,     _A							).sum().item()
			elif pruning_method == "l0":
				snake_case_   ,				snake_case_		   =					-0.1, 1.1
				snake_case_		   =					torch.sigmoid(_A							)
				snake_case_		   =					s * (r - l) + l
				snake_case_		   =					s_bar.clamp(min=0.0					,     max=1.0							)
				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(_A							)							)
		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__":
		lowercase__    :					Optional[int]					=						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",
		)

		lowercase__    :					Dict					=						parser.parse_args()

		main(args)






import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


lowercase__    :					str					=						datasets.utils.logging.get_logger(__name__)
@dataclass
class 		UpperCAmelCase       (    datasets.BuilderConfig      ):







						'''simple docstring'''





						lowerCAmelCase_							=       None







class 		UpperCAmelCase       (    datasets.ArrowBasedBuilder      ):







						'''simple docstring'''





						lowerCAmelCase_							=       ArrowConfig






						def 	snake_case__						(						self							:					Dict						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Union[str, Any]						):






							"""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(__lowercase	,     (str, list, tuple)						):
								snake_case_		   =					data_files
								if isinstance(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) 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(__lowercase	,     __lowercase						):
									snake_case_		   =					[files]
								# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
								snake_case_		   =					[dl_manager.iter_files(__lowercase						) 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(__lowercase						):
										with open(__lowercase	,     "rb"						) as f:
											snake_case_		   =					datasets.Features.from_arrow_schema(pa.ipc.open_stream(__lowercase						).schema						)
										break
								splits.append(datasets.SplitGenerator(name=__lowercase	,     gen_kwargs={"files": files}						)						)
							return splits






						def 	snake_case__						(						self							:					str	,     __lowercase							:					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(__lowercase	,     self.info.features.arrow_schema						)
							return pa_table






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Optional[Any]						):






							"""simple docstring"""






							for file_idx, file in enumerate(itertools.chain.from_iterable(__lowercase						)						):
								with open(__lowercase	,     "rb"						) as f:
									try:
										for batch_idx, record_batch in enumerate(pa.ipc.open_stream(__lowercase						)						):
											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(__lowercase						)
									except ValueError as e:
										logger.error(f"Failed to read file '{file}' with error {type(__lowercase						)}: {e}"						)
										raise
