



"""simple docstring"""


import argparse
import os
import shutil

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    args.pruning_method
				_UpperCAmelCase					=    args.threshold

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

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

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

				if target_model_path is None:
								_UpperCAmelCase					=    os.path.join(
								    os.path.dirname(_SCREAMING_SNAKE_CASE				)					,     f'bertarized_{os.path.basename(_SCREAMING_SNAKE_CASE				)}'				)

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

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


if __name__ == "__main__":
					__A   :							Tuple										=   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   :							List[Any]										=   parser.parse_args()

					main(args)








"""simple docstring"""


from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self     :    List[str]				,   __UpperCamelCase     :    NestedDataStructureLike[PathLike]				,   __UpperCamelCase     :    Optional[NamedSplit] = None				,   __UpperCamelCase     :    Optional[Features] = None				,   __UpperCamelCase     :    str = None				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    Optional[int] = None				,   **__UpperCamelCase     :    Optional[Any]				,   )->Any:
							super().__init__(
							    __UpperCamelCase				,   split=__UpperCamelCase				,   features=__UpperCamelCase				,   cache_dir=__UpperCamelCase				,   keep_in_memory=__UpperCamelCase				,   streaming=__UpperCamelCase				,   num_proc=__UpperCamelCase				,   **__UpperCamelCase				,   )
							_UpperCAmelCase					=    path_or_paths if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else {self.split: path_or_paths}
							_UpperCAmelCase					=    Text(
							    cache_dir=__UpperCamelCase				,   data_files=__UpperCamelCase				,   features=__UpperCamelCase				,   **__UpperCamelCase				,   )



			def  lowercase__						(		self     :    Optional[Any]   )->str:
							# Build iterable dataset
							if self.streaming:
											_UpperCAmelCase					=    self.builder.as_streaming_dataset(split=self.split   )
							# Build regular (map-style) dataset
							else:
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None
											_UpperCAmelCase					=    None

											self.builder.download_and_prepare(
											    download_config=__UpperCamelCase				,   download_mode=__UpperCamelCase				,   verification_mode=__UpperCamelCase				,   base_path=__UpperCamelCase				,   num_proc=self.num_proc				,   )
											_UpperCAmelCase					=    self.builder.as_dataset(
											    split=self.split				,   verification_mode=__UpperCamelCase				,   in_memory=self.keep_in_memory   )
							return dataset



