




import argparse

import torch

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


logging.set_verbosity_info()
_lowerCamelCase      			=						logging.get_logger(__name__)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						Optional[Any]				)  ->							Tuple:
    UpperCAmelCase_										=					WavaVecaForSequenceClassification.from_pretrained(__UpperCamelCase	,					config=__UpperCamelCase				)
    UpperCAmelCase_										=					downstream_dict['''projector.weight''']
    UpperCAmelCase_										=					downstream_dict['''projector.bias''']
    UpperCAmelCase_										=					downstream_dict['''model.post_net.linear.weight''']
    UpperCAmelCase_										=					downstream_dict['''model.post_net.linear.bias''']
    return model




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Union[str, Any]				)  ->							str:
    UpperCAmelCase_										=					WavaVecaForAudioFrameClassification.from_pretrained(__UpperCamelCase	,					config=__UpperCamelCase				)
    UpperCAmelCase_										=					downstream_dict['''model.linear.weight''']
    UpperCAmelCase_										=					downstream_dict['''model.linear.bias''']
    return model




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						List[Any]				)  ->							Optional[int]:
    UpperCAmelCase_										=					WavaVecaForXVector.from_pretrained(__UpperCamelCase	,					config=__UpperCamelCase				)
    UpperCAmelCase_										=					downstream_dict['''connector.weight''']
    UpperCAmelCase_										=					downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
        UpperCAmelCase_										=					downstream_dict[
            f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
        ]
        UpperCAmelCase_										=					downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

    UpperCAmelCase_										=					downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    UpperCAmelCase_										=					downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    UpperCAmelCase_										=					downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    UpperCAmelCase_										=					downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    UpperCAmelCase_										=					downstream_dict['''objective.W''']
    return model




@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						str				)  ->							Any:
    UpperCAmelCase_										=					torch.load(__UpperCamelCase	,					map_location='''cpu'''				)

    UpperCAmelCase_										=					checkpoint['''Downstream''']

    UpperCAmelCase_										=					WavaVecaConfig.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					WavaVecaFeatureExtractor.from_pretrained(
        __UpperCamelCase	,					return_attention_mask=__UpperCamelCase	,					do_normalize=__UpperCamelCase				)

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

    if hf_config.use_weighted_layer_sum:
        UpperCAmelCase_										=					checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(__UpperCamelCase				)
    hf_model.save_pretrained(__UpperCamelCase				)


if __name__ == "__main__":
 _lowerCamelCase      			=						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.')
 _lowerCamelCase      			=						parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)











import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						List[str]=5				)  ->							Dict:
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count('''<mask>'''				) == 1
    UpperCAmelCase_										=					torch.tensor(tokenizer.encode(__UpperCamelCase	,					add_special_tokens=__UpperCamelCase				)				).unsqueeze(0				)  # Batch size 1
    UpperCAmelCase_										=					model(__UpperCamelCase				)[0]  # The last hidden-state is the first element of the output tuple
    UpperCAmelCase_										=					(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    UpperCAmelCase_										=					logits[0, masked_index, :]
    UpperCAmelCase_										=					logits.softmax(dim=0				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					prob.topk(k=__UpperCamelCase	,					dim=0				)
    UpperCAmelCase_										=					''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCamelCase				)				)]				)
    UpperCAmelCase_										=					tokenizer.mask_token
    UpperCAmelCase_										=					[]
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''				)				):
        UpperCAmelCase_										=					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


_lowerCamelCase      			=						CamembertTokenizer.from_pretrained('camembert-base')
_lowerCamelCase      			=						CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_lowerCamelCase      			=						'Le camembert est <mask> :)'


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





