

'''simple docstring'''





A_	= {
    "Pillow": "Pillow<10.0.0",
    "accelerate": "accelerate>=0.20.3",
    "av": "av==9.2.0",
    "beautifulsoup4": "beautifulsoup4",
    "black": "black~=23.1",
    "codecarbon": "codecarbon==1.2.0",
    "cookiecutter": "cookiecutter==1.7.3",
    "dataclasses": "dataclasses",
    "datasets": "datasets!=2.5.0",
    "decord": "decord==0.6.0",
    "deepspeed": "deepspeed>=0.9.3",
    "diffusers": "diffusers",
    "dill": "dill<0.3.5",
    "evaluate": "evaluate>=0.2.0",
    "fairscale": "fairscale>0.3",
    "faiss-cpu": "faiss-cpu",
    "fastapi": "fastapi",
    "filelock": "filelock",
    "flax": "flax>=0.4.1,<=0.7.0",
    "ftfy": "ftfy",
    "fugashi": "fugashi>=1.0",
    "GitPython": "GitPython<3.1.19",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
    "importlib_metadata": "importlib_metadata",
    "ipadic": "ipadic>=1.0.0,<2.0",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
    "jaxlib": "jaxlib>=0.1.65,<=0.4.13",
    "jieba": "jieba",
    "kenlm": "kenlm",
    "keras-nlp": "keras-nlp>=0.3.1",
    "librosa": "librosa",
    "nltk": "nltk",
    "natten": "natten>=0.14.6",
    "numpy": "numpy>=1.17",
    "onnxconverter-common": "onnxconverter-common",
    "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
    "onnxruntime": "onnxruntime>=1.4.0",
    "opencv-python": "opencv-python",
    "optuna": "optuna",
    "optax": "optax>=0.0.8,<=0.1.4",
    "packaging": "packaging>=20.0",
    "parameterized": "parameterized",
    "phonemizer": "phonemizer",
    "protobuf": "protobuf",
    "psutil": "psutil",
    "pyyaml": "pyyaml>=5.1",
    "pydantic": "pydantic<2",
    "pytest": "pytest>=7.2.0",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "python": "python>=3.8.0",
    "ray[tune]": "ray[tune]",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "rhoknp": "rhoknp>=1.1.0,<1.3.1",
    "rjieba": "rjieba",
    "rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
    "ruff": "ruff>=0.0.241,<=0.0.259",
    "sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
    "sacremoses": "sacremoses",
    "safetensors": "safetensors>=0.3.1",
    "sagemaker": "sagemaker>=2.31.0",
    "scikit-learn": "scikit-learn",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "sigopt": "sigopt",
    "starlette": "starlette",
    "sudachipy": "sudachipy>=0.6.6",
    "sudachidict_core": "sudachidict_core>=20220729",
    "tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
    "tensorflow": "tensorflow>=2.6,<2.14",
    "tensorflow-text": "tensorflow-text<2.14",
    "tf2onnx": "tf2onnx",
    "timeout-decorator": "timeout-decorator",
    "timm": "timm",
    "tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
    "torch": "torch>=1.9,!=1.12.0",
    "torchaudio": "torchaudio",
    "torchvision": "torchvision",
    "pyctcdecode": "pyctcdecode>=0.4.0",
    "tqdm": "tqdm>=4.27",
    "unidic": "unidic>=1.0.2",
    "unidic_lite": "unidic_lite>=1.0.7",
    "urllib3": "urllib3<2.0.0",
    "uvicorn": "uvicorn",
}





"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a								=						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""")
				_a								=						parser.parse_args()

				if args.model_type == "bert":
								_a								=						BertForMaskedLM.from_pretrained(args.model_name)
								_a								=						"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a								=						model.state_dict()
				_a								=						{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a								=						state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a								=						state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

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

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a								=						state_dict["""cls.predictions.decoder.weight"""]
				_a								=						state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a								=						state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a								=						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)






