




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
		__UpperCamelCase   :			int          = 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")
		__UpperCamelCase   :			List[str]          = parser.parse_args()

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

		__UpperCamelCase   :			Optional[int]          = model.state_dict()
		__UpperCamelCase   :			str          = {}

		for w in ["word_embeddings", "position_embeddings"]:
				__UpperCamelCase   :			str          = state_dict[F'{prefix}.embeddings.{w}.weight']
		for w in ["weight", "bias"]:
				__UpperCamelCase   :			Optional[Any]          = state_dict[F'{prefix}.embeddings.LayerNorm.{w}']

		__UpperCamelCase   :			Optional[Any]          = 0
		for teacher_idx in [0, 2, 4, 7, 9, 11]:
				for w in ["weight", "bias"]:
						__UpperCamelCase   :			Tuple          = state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
						]
						__UpperCamelCase   :			List[str]          = state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
						]
						__UpperCamelCase   :			Union[str, Any]          = state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
						]

						__UpperCamelCase   :			Union[str, Any]          = state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
						]
						__UpperCamelCase   :			List[Any]          = state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
						]

						__UpperCamelCase   :			Optional[int]          = state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
						]
						__UpperCamelCase   :			str          = state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
						]
						__UpperCamelCase   :			List[Any]          = state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
						]
				std_idx += 1

		__UpperCamelCase   :			Union[str, Any]          = state_dict["cls.predictions.decoder.weight"]
		__UpperCamelCase   :			List[Any]          = state_dict["cls.predictions.bias"]
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						__UpperCamelCase   :			List[Any]          = state_dict[F'cls.predictions.transform.dense.{w}']
						__UpperCamelCase   :			Dict          = 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)







import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version





def 						__A						(      *__lowerCamelCase				,     __lowerCamelCase = None				,     __lowerCamelCase=True				,     __lowerCamelCase=2       ) ->			Dict:
	from .. import __version__

	a									=				take_from
	a									=				()
	if not isinstance(args[0]				,     __lowerCamelCase       ):
		a									=				(args,)

	for attribute, version_name, message in args:
		if version.parse(version.parse(__lowerCamelCase       ).base_version       ) >= version.parse(__lowerCamelCase       ):
			raise ValueError(
			    f'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
			    f' version {__version__} is >= {version_name}'       )

		a									=				None
		if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and attribute in deprecated_kwargs:
			values += (deprecated_kwargs.pop(__lowerCamelCase       ),)
			a									=				f'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
		elif hasattr(__lowerCamelCase				,     __lowerCamelCase       ):
			values += (getattr(__lowerCamelCase				,     __lowerCamelCase       ),)
			a									=				f'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
		elif deprecated_kwargs is None:
			a									=				f'`{attribute}` is deprecated and will be removed in version {version_name}.'

		if warning is not None:
			a									=				warning + """ """ if standard_warn else """"""
			warnings.warn(warning + message				,     __lowerCamelCase				,     stacklevel=__lowerCamelCase       )

	if isinstance(__lowerCamelCase				,     __lowerCamelCase       ) and len(__lowerCamelCase       ) > 0:
		a									=				inspect.getouterframes(inspect.currentframe()       )[1]
		a									=				call_frame.filename
		a									=				call_frame.lineno
		a									=				call_frame.function
		a							,					a									=				next(iter(deprecated_kwargs.items()       )       )
		raise TypeError(f'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'       )

	if len(__lowerCamelCase       ) == 0:
		return
	elif len(__lowerCamelCase       ) == 1:
		return values[0]
	return values

