



import argparse
import os
import shutil

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






def       lowerCAmelCase__						(       lowerCamelCase_						:						Tuple):





				'''simple docstring'''

				lowerCAmelCase__	:							Optional[int]				=	args.pruning_method
				lowerCAmelCase__	:							Union[str, Any]				=	args.threshold

				lowerCAmelCase__	:							List[Any]				=	args.model_name_or_path.rstrip('''/''')
				lowerCAmelCase__	:							List[str]				=	args.target_model_path

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

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

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

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

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


if __name__ == "__main__":
							__snake_case							:						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',
							)

							__snake_case							:						Optional[Any]      =parser.parse_args()

							main(args)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							    =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



