






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Dict							=		argparse.ArgumentParser(
					    description=(
					        """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
					        """ Distillation"""
					    )
					)
					parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
					parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
					parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
					parser.add_argument("""--vocab_transform""", action="""store_true""")
					SCREAMING_SNAKE_CASE__      :			Dict							=		parser.parse_args()

					if args.model_type == "bert":
										SCREAMING_SNAKE_CASE__      :			List[Any]							=		BertForMaskedLM.from_pretrained(args.model_name)
										SCREAMING_SNAKE_CASE__      :			str							=		"""bert"""
					else:
										raise ValueError("""args.model_type should be \"bert\".""")

					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		model.state_dict()
					SCREAMING_SNAKE_CASE__      :			List[str]							=		{}

					for w in ["word_embeddings", "position_embeddings"]:
										SCREAMING_SNAKE_CASE__      :			List[str]							=		state_dict[f"{prefix}.embeddings.{w}.weight"]
					for w in ["weight", "bias"]:
										SCREAMING_SNAKE_CASE__      :			Any							=		state_dict[f"{prefix}.embeddings.LayerNorm.{w}"]

					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		0
					for teacher_idx in [0, 2, 4, 7, 9, 11]:
										for w in ["weight", "bias"]:
															SCREAMING_SNAKE_CASE__      :			int							=		state_dict[
															    f"{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"
															]
															SCREAMING_SNAKE_CASE__      :			Dict							=		state_dict[
															    f"{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"
															]
															SCREAMING_SNAKE_CASE__      :			int							=		state_dict[
															    f"{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"
															]

															SCREAMING_SNAKE_CASE__      :			int							=		state_dict[
															    f"{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"
															]
															SCREAMING_SNAKE_CASE__      :			Tuple							=		state_dict[
															    f"{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"
															]

															SCREAMING_SNAKE_CASE__      :			Dict							=		state_dict[
															    f"{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"
															]
															SCREAMING_SNAKE_CASE__      :			Any							=		state_dict[
															    f"{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"
															]
															SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		state_dict[
															    f"{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"
															]
										std_idx += 1

					SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		state_dict["""cls.predictions.decoder.weight"""]
					SCREAMING_SNAKE_CASE__      :			Tuple							=		state_dict["""cls.predictions.bias"""]
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															SCREAMING_SNAKE_CASE__      :			int							=		state_dict[f"cls.predictions.transform.dense.{w}"]
															SCREAMING_SNAKE_CASE__      :			Optional[int]							=		state_dict[f"cls.predictions.transform.LayerNorm.{w}"]

					print(f"N layers selected for distillation: {std_idx}")
					print(f"Number of params transferred for distillation: {len(compressed_sd.keys())}")

					print(f"Save transferred checkpoint to {args.dump_checkpoint}.")
					torch.save(compressed_sd, args.dump_checkpoint)














from __future__ import annotations

from math import pi, sqrt

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if inductance <= 0:
								raise ValueError('''Inductance cannot be 0 or negative''' )

				elif capacitance <= 0:
								raise ValueError('''Capacitance cannot be 0 or negative''' )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






