



import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Any				)->Dict:
						_lowerCAmelCase 			=  args.pruning_method
						_lowerCAmelCase 			=  args.threshold

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

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

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

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

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

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


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

				UpperCAmelCase_									=							parser.parse_args()

				main(args)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"processing_layoutxlm": ["LayoutXLMProcessor"]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["LayoutXLMTokenizer"]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["LayoutXLMTokenizerFast"]

if TYPE_CHECKING:
    from .processing_layoutxlm import LayoutXLMProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm import LayoutXLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
    import sys

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