


import argparse

import torch

from transformers import BertForMaskedLM


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

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

		__UpperCAmelCase       =							model.state_dict()
		__UpperCAmelCase       =							{}

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

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

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

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

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






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				"distilbert"
							lowercase__      :       Tuple     						=				{
							    "hidden_size": "dim",
							    "num_attention_heads": "n_heads",
							    "num_hidden_layers": "n_layers",
							}







							def __init__(		self ,   lowerCamelCase_=3_05_22 ,   lowerCamelCase_=5_12 ,   lowerCamelCase_=False ,   lowerCamelCase_=6 ,   lowerCamelCase_=12 ,   lowerCamelCase_=7_68 ,   lowerCamelCase_=4 * 7_68 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=0.02 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=0.2 ,   lowerCamelCase_=0 ,   **lowerCamelCase_ ,   )	->							Any:
														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     max_position_embeddings
														lowerCAmelCase__	       =     sinusoidal_pos_embds
														lowerCAmelCase__	       =     n_layers
														lowerCAmelCase__	       =     n_heads
														lowerCAmelCase__	       =     dim
														lowerCAmelCase__	       =     hidden_dim
														lowerCAmelCase__	       =     dropout
														lowerCAmelCase__	       =     attention_dropout
														lowerCAmelCase__	       =     activation
														lowerCAmelCase__	       =     initializer_range
														lowerCAmelCase__	       =     qa_dropout
														lowerCAmelCase__	       =     seq_classif_dropout
														super().__init__(**lowerCamelCase_ ,   pad_token_id=lowerCamelCase_       )




class 		a__    (							a__  ):





							'''simple docstring'''






							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Mapping[str, Mapping[int, str]]:
														if self.task == "multiple-choice":
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					lowerCAmelCase__	       =     {0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														    ]       )