


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						snake_case							:	Tuple					      =				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''')
						snake_case							:	List[str]					      =				parser.parse_args()

						if args.model_type == "bert":
												snake_case							:	Optional[int]					      =				BertForMaskedLM.from_pretrained(args.model_name)
												snake_case							:	List[Any]					      =				'''bert'''
						else:
												raise ValueError('''args.model_type should be "bert".''')

						snake_case							:	Any					      =				model.state_dict()
						snake_case							:	str					      =				{}

						for w in ["word_embeddings", "position_embeddings"]:
												snake_case							:	Dict					      =				state_dict[f"""{prefix}.embeddings.{w}.weight"""]
						for w in ["weight", "bias"]:
												snake_case							:	Optional[int]					      =				state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

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

																		snake_case							:	str					      =				state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
																		]
																		snake_case							:	Optional[Any]					      =				state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
																		]

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

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case							:	Optional[int]					      =				{
    '''configuration_clipseg''': [
        '''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPSegConfig''',
        '''CLIPSegTextConfig''',
        '''CLIPSegVisionConfig''',
    ],
    '''processing_clipseg''': ['''CLIPSegProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:	List[str]					      =				[
						    '''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''CLIPSegModel''',
						    '''CLIPSegPreTrainedModel''',
						    '''CLIPSegTextModel''',
						    '''CLIPSegVisionModel''',
						    '''CLIPSegForImageSegmentation''',
						]

if TYPE_CHECKING:
						from .configuration_clipseg import (
						    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    CLIPSegConfig,
						    CLIPSegTextConfig,
						    CLIPSegVisionConfig,
						)
						from .processing_clipseg import CLIPSegProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_clipseg import (
												    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CLIPSegForImageSegmentation,
												    CLIPSegModel,
												    CLIPSegPreTrainedModel,
												    CLIPSegTextModel,
												    CLIPSegVisionModel,
												)

else:
						import sys

						snake_case							:	Optional[int]					      =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




