





import argparse

import torch

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


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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    List[str]   )   ->			List[Any]:
    UpperCamelCase__							:       Tuple      =   WavaVecaForSequenceClassification.from_pretrained(_UpperCAmelCase	,     config=_UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   downstream_dict['''projector.weight''']
    UpperCamelCase__							:       int      =   downstream_dict['''projector.bias''']
    UpperCamelCase__							:       List[Any]      =   downstream_dict['''model.post_net.linear.weight''']
    UpperCamelCase__							:       Dict      =   downstream_dict['''model.post_net.linear.bias''']
    return model




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Optional[Any]   )   ->			List[str]:
    UpperCamelCase__							:       List[str]      =   WavaVecaForAudioFrameClassification.from_pretrained(_UpperCAmelCase	,     config=_UpperCAmelCase   )
    UpperCamelCase__							:       Any      =   downstream_dict['''model.linear.weight''']
    UpperCamelCase__							:       int      =   downstream_dict['''model.linear.bias''']
    return model




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    Dict   )   ->			List[Any]:
    UpperCamelCase__							:       int      =   WavaVecaForXVector.from_pretrained(_UpperCAmelCase	,     config=_UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   downstream_dict['''connector.weight''']
    UpperCamelCase__							:       Tuple      =   downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
        UpperCamelCase__							:       Optional[Any]      =   downstream_dict[
            f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
        ]
        UpperCamelCase__							:       Optional[Any]      =   downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

    UpperCamelCase__							:       int      =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    UpperCamelCase__							:       int      =   downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    UpperCamelCase__							:       str      =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    UpperCamelCase__							:       Union[str, Any]      =   downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    UpperCamelCase__							:       Union[str, Any]      =   downstream_dict['''objective.W''']
    return model




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Optional[int]   )   ->			Any:
    UpperCamelCase__							:       List[str]      =   torch.load(_UpperCAmelCase	,     map_location='''cpu'''   )

    UpperCamelCase__							:       List[str]      =   checkpoint['''Downstream''']

    UpperCamelCase__							:       List[Any]      =   WavaVecaConfig.from_pretrained(_UpperCAmelCase   )
    UpperCamelCase__							:       Optional[int]      =   WavaVecaFeatureExtractor.from_pretrained(
        _UpperCAmelCase	,     return_attention_mask=_UpperCAmelCase	,     do_normalize=_UpperCAmelCase   )

    UpperCamelCase__							:       Tuple      =   hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''   ):
        UpperCamelCase__							:       List[Any]      =   convert_classification(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase   )
    elif arch.endswith('''ForAudioFrameClassification'''   ):
        UpperCamelCase__							:       List[str]      =   convert_diarization(_UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase   )
    elif arch.endswith('''ForXVector'''   ):
        UpperCamelCase__							:       Union[str, Any]      =   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__							:       List[str]      =   checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(_UpperCAmelCase   )
    hf_model.save_pretrained(_UpperCAmelCase   )


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








import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    str   )   ->			Optional[int]:
    # Initialise PyTorch model.
    # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
    # TapasConfig to False.

    # initialize configuration from json file
    UpperCamelCase__							:       Any      =   TapasConfig.from_json_file(__UpperCAmelCase   )
    # set absolute/relative position embeddings parameter
    UpperCamelCase__							:       Optional[Any]      =   reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        UpperCamelCase__							:       List[Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WTQ":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   True
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   0.664694
        UpperCamelCase__							:       Union[str, Any]      =   0.207951
        UpperCamelCase__							:       Any      =   0.121194
        UpperCamelCase__							:       int      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       str      =   False
        UpperCamelCase__							:       int      =   0.0352513

        UpperCamelCase__							:       Dict      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   False
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   36.4519
        UpperCamelCase__							:       int      =   0.903421
        UpperCamelCase__							:       List[str]      =   222.088
        UpperCamelCase__							:       Dict      =   True
        UpperCamelCase__							:       List[str]      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       int      =   0.763141

        UpperCamelCase__							:       Union[str, Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "TABFACT":
        UpperCamelCase__							:       List[Any]      =   TapasForSequenceClassification(config=__UpperCAmelCase   )
    elif task == "MLM":
        UpperCamelCase__							:       Optional[Any]      =   TapasForMaskedLM(config=__UpperCAmelCase   )
    elif task == "INTERMEDIATE_PRETRAINING":
        UpperCamelCase__							:       str      =   TapasModel(config=__UpperCAmelCase   )
    else:
        raise ValueError(f"Task {task} not supported."   )

    print(f"Building PyTorch model from configuration: {config}"   )
    # Load weights from tf checkpoint
    load_tf_weights_in_tapas(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

    # Save pytorch-model (weights and configuration)
    print(f"Save PyTorch model to {pytorch_dump_path}"   )
    model.save_pretrained(__UpperCAmelCase   )

    # Save tokenizer files
    print(f"Save tokenizer files to {pytorch_dump_path}"   )
    UpperCamelCase__							:       str      =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt'''	,     model_max_length=512   )
    tokenizer.save_pretrained(__UpperCAmelCase   )

    print('''Used relative position embeddings:'''	,     model.config.reset_position_index_per_cell   )


if __name__ == "__main__":
  UpperCAmelCase_  							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
  )
  parser.add_argument(
      '--reset_position_index_per_cell',
      default=False,
      action='store_true',
      help='Whether to use relative position embeddings or not. Defaults to True.',
  )
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--tapas_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained TAPAS model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )

