



'''simple docstring'''



import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def 	__lowerCAmelCase       (				lowerCamelCase :    Union[str, Any]       ):






	'''simple docstring'''




	__lowerCAmelCase       				=	args.serialization_dir
	__lowerCAmelCase       				=	args.pruning_method
	__lowerCAmelCase       				=	args.threshold

	__lowerCAmelCase       				=	torch.load(os.path.join(UpperCAmelCase_					,		"pytorch_model.bin"       )					,		map_location="cpu"       )

	__lowerCAmelCase       				=	0  # Number of remaining (not pruned) params in the encoder
	__lowerCAmelCase       				=	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":
				__lowerCAmelCase       				=	TopKBinarizer.apply(UpperCAmelCase_					,		UpperCAmelCase_       ).sum().item()
			elif pruning_method == "sigmoied_threshold":
				__lowerCAmelCase       				=	ThresholdBinarizer.apply(UpperCAmelCase_					,		UpperCAmelCase_					,		UpperCAmelCase_       ).sum().item()
			elif pruning_method == "l0":
				__lowerCAmelCase				,							__lowerCAmelCase       				=	-0.1, 1.1
				__lowerCAmelCase       				=	torch.sigmoid(UpperCAmelCase_       )
				__lowerCAmelCase       				=	s * (r - l) + l
				__lowerCAmelCase       				=	s_bar.clamp(min=0.0					,		max=1.0       )
				__lowerCAmelCase       				=	(mask > 0.0).sum().item()
			else:
				raise ValueError("Unknown pruning method"       )
			remaining_count += mask_ones
			print(name.ljust(60					,		" "       )					,		str(round(1_00 * mask_ones / param.numel()					,		3       )       ).ljust(20					,		" "       )					,		str(UpperCAmelCase_       )       )
		else:
			encoder_count += param.numel()
			if "bias" in name or "LayerNorm" in name:
				remaining_count += param.numel()

	print(""       )
	print("Remaining Weights (global) %: "					,		1_00 * remaining_count / encoder_count       )


if __name__ == "__main__":
	lowerCAmelCase						:		Dict						      = 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''',
	)

	lowerCAmelCase						:		List[Any]						      = parser.parse_args()

	main(args)


"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    [
     'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GPTJForCausalLM',
     'GPTJForQuestionAnswering',
     'GPTJForSequenceClassification',
     'GPTJModel',
     'GPTJPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    [
     'TFGPTJForCausalLM',
     'TFGPTJForQuestionAnswering',
     'TFGPTJForSequenceClassification',
     'TFGPTJModel',
     'TFGPTJPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Tuple					=    [
     'FlaxGPTJForCausalLM',
     'FlaxGPTJModel',
     'FlaxGPTJPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gptj import (
      GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTJForCausalLM,
      GPTJForQuestionAnswering,
      GPTJForSequenceClassification,
      GPTJModel,
      GPTJPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      TFGPTJPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
 import sys

 SCREAMING_SNAKE_CASE_       :						List[str]					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






