
"""simple docstring"""



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a					:	Union[str, Any]							=		logging.get_logger(__name__)

a					:	Optional[Any]							=		{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
a					:	Optional[Any]							=		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    for attribute in key.split("""."""      ):
        _UpperCAmelCase          =   getattr(_A					,      _A      )

    if weight_type is not None:
        _UpperCAmelCase          =   getattr(_A					,      _A      ).shape
    else:
        _UpperCAmelCase          =   hf_pointer.shape

    assert hf_shape == value.shape, (
        F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
        F""" {value.shape} for {full_name}"""
    )

    if weight_type == "weight":
        _UpperCAmelCase          =   value
    elif weight_type == "weight_g":
        _UpperCAmelCase          =   value
    elif weight_type == "weight_v":
        _UpperCAmelCase          =   value
    elif weight_type == "bias":
        _UpperCAmelCase          =   value
    else:
        _UpperCAmelCase          =   value

    logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""      )


def  _UpperCamelCase	(						_A					,      _A      )    ->  Optional[int]:





    """simple docstring"""

    _UpperCAmelCase          =   []
    _UpperCAmelCase          =   fairseq_model.state_dict()

    _UpperCAmelCase          =   hf_model.feature_extractor
    _UpperCAmelCase          =   hf_model.adapter

    for name, value in fairseq_dict.items():
        _UpperCAmelCase          =   False
        if "conv_layers" in name:
            load_conv_layer(
                _A					,      _A					,      _A					,      _A					,      hf_model.config.feat_extract_norm == """group"""					,      )
            _UpperCAmelCase          =   True
        elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]      ):
            load_adapter(_A					,      _A					,      _A					,      _A      )
            _UpperCAmelCase          =   True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
                    _UpperCAmelCase          =   True
                    if "*" in mapped_key:
                        _UpperCAmelCase          =   name.split(_A      )[0].split("""."""      )[-2]
                        _UpperCAmelCase          =   mapped_key.replace("""*"""					,      _A      )
                    if "weight_g" in name:
                        _UpperCAmelCase          =   """weight_g"""
                    elif "weight_v" in name:
                        _UpperCAmelCase          =   """weight_v"""
                    elif "bias" in name:
                        _UpperCAmelCase          =   """bias"""
                    elif "weight" in name:
                        _UpperCAmelCase          =   """weight"""
                    else:
                        _UpperCAmelCase          =   None
                    set_recursively(_A					,      _A					,      _A					,      _A					,      _A      )
                continue
        if not is_used:
            unused_weights.append(_A      )

    logger.warning(F"""Unused weights: {unused_weights}"""      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase          =   full_name.split("""conv_layers."""      )[-1]
    _UpperCAmelCase          =   name.split("""."""      )
    _UpperCAmelCase          =   int(items[0]      )
    _UpperCAmelCase          =   int(items[1]      )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
            )
            _UpperCAmelCase          =   value
            logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
            )
            _UpperCAmelCase          =   value
            logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                " found."
            )
            _UpperCAmelCase          =   value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
            )
            _UpperCAmelCase          =   value
            logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
    else:
        unused_weights.append(_A      )


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   full_name.split("""adaptor."""      )[-1]
    _UpperCAmelCase          =   name.split("""."""      )

    if items[1].isdigit():
        _UpperCAmelCase          =   int(items[1]      )
    else:
        _UpperCAmelCase          =   None

    if "adaptor" not in full_name:
        if "proj_ln" in full_name:
            # has to be layer norm
            if "bias" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.bias.data.shape
                ), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
                _UpperCAmelCase          =   value
                logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""      )
            if "weight" in name:
                assert (
                    value.shape == adapter.proj_layer_norm.weight.data.shape
                ), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
                _UpperCAmelCase          =   value
        else:
            # has to be projection layer
            if "bias" in name:
                assert (
                    value.shape == adapter.proj.bias.data.shape
                ), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
                _UpperCAmelCase          =   value
                logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""      )
            if "weight" in name:
                assert (
                    value.shape == adapter.proj.weight.data.shape
                ), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
                _UpperCAmelCase          =   value
                logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""      )
    elif isinstance(_A					,      _A      ):
        if "bias" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.bias.data.shape
            ), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
            _UpperCAmelCase          =   value
            logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""      )
        elif "weight" in name:
            assert (
                value.shape == adapter.layers[layer_id].conv.weight.data.shape
            ), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
            _UpperCAmelCase          =   value
            logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""      )
    else:
        unused_weights.append(_A      )


def  _UpperCamelCase	(						_A      )    ->  Tuple:





    """simple docstring"""

    _UpperCAmelCase    ,_UpperCAmelCase          =   emb.weight.shape
    _UpperCAmelCase          =   nn.Linear(_A					,      _A					,      bias=_A      )
    _UpperCAmelCase          =   emb.weight.data
    return lin_layer


@torch.no_grad()
def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      _A					,      )    ->  List[str]:





    """simple docstring"""

    _UpperCAmelCase          =   WavaVecaConfig.from_pretrained(
        _A					,      add_adapter=_A					,      adapter_stride=_A					,      adapter_kernel_size=_A					,      use_auth_token=_A					,      output_hidden_size=_A					,      )
    _UpperCAmelCase          =   MBartConfig.from_pretrained(_A      )

    # load model
    _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]					,      arg_overrides={
            """config_yaml""": config_yaml_path,
            """data""": """/""".join(dict_path.split("""/"""      )[:-1]      ),
            """w2v_path""": checkpoint_path,
            """load_pretrained_decoder_from""": None,
        }					,      )
    _UpperCAmelCase          =   model[0].eval()

    # load feature extractor
    _UpperCAmelCase          =   WavaVecaFeatureExtractor.from_pretrained(_A					,      use_auth_token=_A      )

    # set weights for wav2vec2 encoder
    _UpperCAmelCase          =   WavaVecaModel(_A      )

    recursively_load_weights_wavaveca(model.encoder					,      _A      )

    # load decoder weights
    _UpperCAmelCase          =   MBartForCausalLM(_A      )
    _UpperCAmelCase    ,_UpperCAmelCase          =   hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					,      strict=_A      )
    logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""      )
    logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""      )

    _UpperCAmelCase          =   SpeechEncoderDecoderModel(encoder=_A					,      decoder=_A      )
    _UpperCAmelCase          =   False

    _UpperCAmelCase          =   MBartaaTokenizer(_A      )
    tokenizer.save_pretrained(_A      )

    _UpperCAmelCase          =   hf_wavavec.config.to_dict()
    _UpperCAmelCase          =   tokenizer.pad_token_id
    _UpperCAmelCase          =   tokenizer.bos_token_id
    _UpperCAmelCase          =   tokenizer.eos_token_id
    _UpperCAmelCase          =   """mbart50"""
    _UpperCAmelCase          =   """wav2vec2"""

    _UpperCAmelCase          =   tokenizer.eos_token_id
    _UpperCAmelCase          =   2_5_0_0_0_4
    _UpperCAmelCase          =   tokenizer.eos_token_id

    _UpperCAmelCase          =   SpeechEncoderDecoderConfig.from_dict(_A      )

    hf_wavavec.save_pretrained(_A      )
    feature_extractor.save_pretrained(_A      )


if __name__ == "__main__":
 a					:	List[Any]							=		argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
 parser.add_argument(
     '''--encoder_config_path''',
     default='''facebook/wav2vec2-xls-r-1b''',
     type=str,
     help='''Path to hf encoder wav2vec2 checkpoint config''',
 )
 parser.add_argument(
     '''--decoder_config_path''',
     default='''facebook/mbart-large-50-one-to-many-mmt''',
     type=str,
     help='''Path to hf decoder checkpoint config''',
 )
 parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
 parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
 parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
 parser.add_argument('''--encoder_output_dim''', default=1_0_2_4, type=int, help='''encoder output dim''')
 parser.add_argument('''--start_token_id''', default=2_5_0_0_0_4, type=int, help='''`decoder_start_token_id` of model config''')

 a					:	Tuple							=		parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     args.config_yaml_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     add_adapter=args.add_adapter,
     adapter_kernel_size=args.adapter_kernel_size,
     adapter_stride=args.adapter_stride,
     decoder_start_token_id=args.start_token_id,
     encoder_output_dim=args.encoder_output_dim,
 )

"""simple docstring"""



import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def  _UpperCamelCase	(						_A					,      _A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   LxmertConfig.from_json_file(_A      )
    print(F"""Building PyTorch model from configuration: {config}"""      )
    _UpperCAmelCase          =   LxmertForPreTraining(_A      )

    # Load weights from tf checkpoint
    load_tf_weights_in_lxmert(_A					,      _A					,      _A      )

    # Save pytorch-model
    print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
    torch.save(model.state_dict()					,      _A      )


if __name__ == "__main__":
 a					:	Dict							=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a					:	Union[str, Any]							=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)