


'''simple docstring'''





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_lowercase       :      Optional[int]	=	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")
				_lowercase       :      Optional[Any]	=	parser.parse_args()

				if args.model_type == "bert":
								_lowercase       :      str	=	BertForMaskedLM.from_pretrained(args.model_name)
								_lowercase       :      List[str]	=	"bert"
				else:
								raise ValueError("args.model_type should be \"bert\".")

				_lowercase       :      str	=	model.state_dict()
				_lowercase       :      List[Any]	=	{}

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

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

												_lowercase       :      List[str]	=	state_dict[
												    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
												]
												_lowercase       :      List[str]	=	state_dict[
												    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
												]

												_lowercase       :      Optional[int]	=	state_dict[
												    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
												]
												_lowercase       :      Tuple	=	state_dict[
												    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
												]
												_lowercase       :      Optional[Any]	=	state_dict[
												    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
												]
								std_idx += 1

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







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







	assert masked_input.count("""<mask>"""						) == 1
	A								=				torch.tensor(tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
	A								=				model(UpperCamelCase__						)[0]  # The last hidden-state is the first element of the output tuple
	A								=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	A								=				logits[0, masked_index, :]
	A								=				logits.softmax(dim=0						)
	A					,			A								=				prob.topk(k=UpperCamelCase__							,					dim=0						)
	A								=				""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
	A								=				tokenizer.mask_token
	A								=				[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """						)						):
		A								=				predicted_token_bpe.replace("""\u2581"""							,					""" """						)
		if " {0}".format(UpperCamelCase__						) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(UpperCamelCase__						)							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(UpperCamelCase__							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
	return topk_filled_outputs


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



