




import argparse

import torch

from transformers import BertForMaskedLM


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

		if args.model_type == "bert":
				__UpperCamelCase					:       Tuple    					=      BertForMaskedLM.from_pretrained(args.model_name)
				__UpperCamelCase					:       Union[str, Any]    					=      "bert"
		else:
				raise ValueError("args.model_type should be \"bert\".")

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

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

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

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

						__UpperCamelCase					:       List[Any]    					=      state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
						]
						__UpperCamelCase					:       List[Any]    					=      state_dict[
						    F'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
						]
						__UpperCamelCase					:       List[str]    					=      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					:       Tuple    					=      state_dict["cls.predictions.bias"]
		if args.vocab_transform:
				for w in ["weight", "bias"]:
						__UpperCamelCase					:       Optional[int]    					=      state_dict[F'cls.predictions.transform.dense.{w}']
						__UpperCamelCase					:       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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase					:       Optional[Any]    					=      {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase					:       List[Any]    					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

