





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
	lowerCAmelCase_    =				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''')
	lowerCAmelCase_    =				parser.parse_args()

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

	lowerCAmelCase_    =				model.state_dict()
	lowerCAmelCase_    =				{}

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

	lowerCAmelCase_    =				0
	for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
		for w in ["weight", "bias"]:
			lowerCAmelCase_    =				state_dict[
			    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
			]
			lowerCAmelCase_    =				state_dict[
			    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
			]
			lowerCAmelCase_    =				state_dict[
			    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
			]

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

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

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




'''simple docstring'''



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class    UpperCAmelCase_  (_UpperCAmelCase					,		_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  :					Union[str, Any]							     =			VQModel
    lowerCamelCase  :					Union[str, Any]							     =			'sample'







    @property
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=(32, 32)	)				->     Any:
         __lowerCamelCase   :	Tuple								=		4
         __lowerCamelCase   :	Union[str, Any]								=		3

         __lowerCamelCase   :	List[Any]								=		floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE_	)

         return {"sample": image}







    @property
    def       lowercase_				(							self	)				->     Optional[int]:
         return (3, 32, 32)







    @property
    def       lowercase_				(							self	)				->     List[Any]:
         return (3, 32, 32)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Optional[Any]								=		{
             'block_out_channels': [32, 64],
             'in_channels': 3,
             'out_channels': 3,
             'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
             'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
             'latent_channels': 3,
         }
         __lowerCamelCase   :	Union[str, Any]								=		self.dummy_input
         return init_dict, inputs_dict







    def       lowercase_				(							self	)				->     str:
         pass







    def       lowercase_				(							self	)				->     Optional[int]:
         pass







    def       lowercase_				(							self	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Union[str, Any]								=		VQModel.from_pretrained('fusing/vqgan-dummy'			,     output_loading_info=SCREAMING_SNAKE_CASE_	)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(len(loading_info['missing_keys']	)			,     0	)

         model.to(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[Any]								=		model(**self.dummy_input	)

         assert image is not None, "Make sure output is not None"







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	List[Any]								=		VQModel.from_pretrained('fusing/vqgan-dummy'	)
         model.to(SCREAMING_SNAKE_CASE_	).eval()

         torch.manual_seed(0	)
         if torch.cuda.is_available():
              torch.cuda.manual_seed_all(0	)

         __lowerCamelCase   :	List[str]								=		torch.randn(1			,     model.config.in_channels			,     model.config.sample_size			,     model.config.sample_size	)
         __lowerCamelCase   :	Optional[int]								=		image.to(SCREAMING_SNAKE_CASE_	)
         with torch.no_grad():
              __lowerCamelCase   :	int								=		model(SCREAMING_SNAKE_CASE_	).sample

         __lowerCamelCase   :	List[str]								=		output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         __lowerCamelCase   :	Union[str, Any]								=		torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]	)
         # fmt: on
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-3	)	)






