




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=5    )      ->       int:
	# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
	assert masked_input.count("""<mask>"""    ) == 1
	a										=							torch.tensor(tokenizer.encode(__lowerCamelCase							,			add_special_tokens=__lowerCamelCase    )    ).unsqueeze(0    )  # Batch size 1
	a										=							model(__lowerCamelCase    )[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=__lowerCamelCase							,			dim=0    )
	a										=							""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()    ) for i in range(len(__lowerCamelCase    )    )]    )
	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(__lowerCamelCase    ) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(__lowerCamelCase    )							,			__lowerCamelCase    ),
			        values[index].item(),
			        predicted_token,
			    )    )
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(__lowerCamelCase							,			__lowerCamelCase    ),
			        values[index].item(),
			        predicted_token,
			    )    )
	return topk_filled_outputs


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

__UpperCamelCase					:       Dict    					=      "Le camembert est <mask> :)"



print(fill_mask(masked_input, model, tokenizer, topk=3))







import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForSequenceClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""projector.weight"""]
	a										=							downstream_dict["""projector.bias"""]
	a										=							downstream_dict["""model.post_net.linear.weight"""]
	a										=							downstream_dict["""model.post_net.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							WavaVecaForAudioFrameClassification.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""model.linear.weight"""]
	a										=							downstream_dict["""model.linear.bias"""]
	return model





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[Any]:
	a										=							WavaVecaForXVector.from_pretrained(__lowerCamelCase							,			config=__lowerCamelCase    )
	a										=							downstream_dict["""connector.weight"""]
	a										=							downstream_dict["""connector.bias"""]
	for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
		a										=							downstream_dict[
		    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
		]
		a										=							downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
	a										=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
	a										=							downstream_dict["""objective.W"""]
	return model





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							torch.load(__lowerCamelCase							,			map_location="""cpu"""    )

	a										=							checkpoint["""Downstream"""]

	a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	a										=							WavaVecaFeatureExtractor.from_pretrained(
	    __lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			do_normalize=__lowerCamelCase    )

	a										=							hf_config.architectures[0]
	if arch.endswith("""ForSequenceClassification"""    ):
		a										=							convert_classification(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	elif arch.endswith("""ForAudioFrameClassification"""    ):
		a										=							convert_diarization(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	elif arch.endswith("""ForXVector"""    ):
		a										=							convert_xvector(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	else:
		raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'    )

	if hf_config.use_weighted_layer_sum:
		a										=							checkpoint["""Featurizer"""]["""weights"""]

	hf_feature_extractor.save_pretrained(__lowerCamelCase    )
	hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       List[str]    					=      argparse.ArgumentParser()
		parser.add_argument(
		    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
		)
		parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
		parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
		__UpperCamelCase					:       List[Any]    					=      parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

