
"""simple docstring"""





import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict



@pytest.mark.parametrize(
    '''split_dict''',     [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train''',     num_bytes=13_37,     num_examples=42,     dataset_name='''my_dataset'''	)}	),
        SplitDict({'''train''': SplitInfo(name='''train''',     num_bytes=13_37,     num_examples=42	)}	),
        SplitDict({'''train''': SplitInfo()}	),
    ],     )
def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





					_UpperCamelCase							 =  split_dict._to_yaml_list()
					assert len(__snake_case	) == len(__snake_case	)
					_UpperCamelCase							 =  SplitDict._from_yaml_list(__snake_case	)
					for split_name, split_info in split_dict.items():
										# dataset_name field is deprecated, and is therefore not part of the YAML dump
										_UpperCamelCase							 =  None
										# the split name of split_dict takes over the name of the split info object
										_UpperCamelCase							 =  split_name
					assert split_dict == reloaded



@pytest.mark.parametrize(
    '''split_info''',     [SplitInfo(), SplitInfo(dataset_name=__snake_case	), SplitInfo(dataset_name='''my_dataset'''	)]	)
def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





					_UpperCamelCase							 =  asdict(SplitDict({'''train''': split_info}	)	)
					assert "dataset_name" in split_dict_asdict["train"]
					assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name








"""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)






