
"""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		:       Tuple     						=       logging.get_logger(__name__)

a		:       int     						=       {
    '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		:       Dict     						=       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		int							,		UpperCamelCase     :		int							,		UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		int     )     ->						Dict:
    for attribute in key.split('.'     ):
        a__		=			getattr(UpperCamelCase							,		UpperCamelCase     )

    if weight_type is not None:
        a__		=			getattr(UpperCamelCase							,		UpperCamelCase     ).shape
    else:
        a__		=			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":
        a__		=			value
    elif weight_type == "weight_g":
        a__		=			value
    elif weight_type == "weight_v":
        a__		=			value
    elif weight_type == "bias":
        a__		=			value
    else:
        a__		=			value

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



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		Union[str, Any]     )     ->						List[Any]:
    a__		=			[]
    a__		=			fairseq_model.state_dict()

    a__		=			hf_model.feature_extractor
    a__		=			hf_model.adapter

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

    logger.warning(f'Unused weights: {unused_weights}'     )



def     __magic_name__		(					UpperCamelCase     :		Union[str, Any]							,		UpperCamelCase     :		Union[str, Any]							,		UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Union[str, Any]     )     ->						Optional[int]:
    a__		=			full_name.split('conv_layers.'     )[-1]
    a__		=			name.split('.'     )
    a__		=			int(items[0]     )
    a__		=			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.'
            )
            a__		=			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.'
            )
            a__		=			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."
            )
            a__		=			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.'
            )
            a__		=			value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
    else:
        unused_weights.append(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		Tuple							,		UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		Union[str, Any]							,		UpperCamelCase     :		str     )     ->						Optional[Any]:
    a__		=			full_name.split('adaptor.'     )[-1]
    a__		=			name.split('.'     )

    if items[1].isdigit():
        a__		=			int(items[1]     )
    else:
        a__		=			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.'
                a__		=			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.'
                a__		=			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.'
                a__		=			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.'
                a__		=			value
                logger.info(f'Adapter proj layer weight was initialized from {full_name}.'     )
    elif isinstance(UpperCamelCase							,		UpperCamelCase     ):
        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.'
            a__		=			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.'
            a__		=			value
            logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'     )
    else:
        unused_weights.append(UpperCamelCase     )



def     __magic_name__		(					UpperCamelCase     :		List[str]     )     ->						Any:
    a__	,     a__		=			emb.weight.shape
    a__		=			nn.Linear(UpperCamelCase							,		UpperCamelCase							,		bias=UpperCamelCase     )
    a__		=			emb.weight.data
    return lin_layer



@torch.no_grad()
def     __magic_name__		(					UpperCamelCase     :		Tuple							,		UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		Tuple							,		UpperCamelCase     :		int							,		UpperCamelCase     :		Union[str, Any]							,		UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		Union[str, Any]							,		UpperCamelCase     :		int							,		UpperCamelCase     :		Optional[Any]							,		UpperCamelCase     :		Dict							,		)     ->						Tuple:
    a__		=			WavaVecaConfig.from_pretrained(
        UpperCamelCase							,		add_adapter=UpperCamelCase							,		adapter_stride=UpperCamelCase							,		adapter_kernel_size=UpperCamelCase							,		use_auth_token=UpperCamelCase							,		output_hidden_size=UpperCamelCase							,		)
    a__		=			MBartConfig.from_pretrained(UpperCamelCase     )

    # load model
    a__	,     a__	,     a__		=			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,
        }							,		)
    a__		=			model[0].eval()

    # load feature extractor
    a__		=			WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase							,		use_auth_token=UpperCamelCase     )

    # set weights for wav2vec2 encoder
    a__		=			WavaVecaModel(UpperCamelCase     )

    recursively_load_weights_wavaveca(model.encoder							,		UpperCamelCase     )

    # load decoder weights
    a__		=			MBartForCausalLM(UpperCamelCase     )
    a__	,     a__		=			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,		strict=UpperCamelCase     )
    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}'     )

    a__		=			SpeechEncoderDecoderModel(encoder=UpperCamelCase							,		decoder=UpperCamelCase     )
    a__		=			False

    a__		=			MBartaaTokenizer(UpperCamelCase     )
    tokenizer.save_pretrained(UpperCamelCase     )

    a__		=			hf_wavavec.config.to_dict()
    a__		=			tokenizer.pad_token_id
    a__		=			tokenizer.bos_token_id
    a__		=			tokenizer.eos_token_id
    a__		=			'mbart50'
    a__		=			'wav2vec2'

    a__		=			tokenizer.eos_token_id
    a__		=			250004
    a__		=			tokenizer.eos_token_id

    a__		=			SpeechEncoderDecoderConfig.from_dict(UpperCamelCase     )

    hf_wavavec.save_pretrained(UpperCamelCase     )
    feature_extractor.save_pretrained(UpperCamelCase     )


if __name__ == "__main__":
    a		:       Tuple     						=       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=10_24, type=int, help='encoder output dim')
    parser.add_argument('--start_token_id', default=25_00_04, type=int, help='`decoder_start_token_id` of model config')

    a		:       Any     						=       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 torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class     lowercase(_lowercase    ,						_lowercase    ,						_lowercase       ):



     @register_to_config
     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = False     ,				)		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			nn.Embedding(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			False

         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )

         a__		=			TaConfig(
             vocab_size=__SCREAMING_SNAKE_CASE     ,				d_model=__SCREAMING_SNAKE_CASE     ,				num_heads=__SCREAMING_SNAKE_CASE     ,				d_kv=__SCREAMING_SNAKE_CASE     ,				d_ff=__SCREAMING_SNAKE_CASE     ,				dropout_rate=__SCREAMING_SNAKE_CASE     ,				feed_forward_proj=__SCREAMING_SNAKE_CASE     ,				is_decoder=__SCREAMING_SNAKE_CASE     ,				is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,				)

         a__		=			nn.ModuleList()
         for lyr_num in range(__SCREAMING_SNAKE_CASE   ):
             a__		=			TaBlock(__SCREAMING_SNAKE_CASE   )
             self.encoders.append(__SCREAMING_SNAKE_CASE   )

         a__		=			TaLayerNorm(__SCREAMING_SNAKE_CASE   )
         a__		=			nn.Dropout(p=__SCREAMING_SNAKE_CASE   )




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			self.token_embedder(__SCREAMING_SNAKE_CASE   )

         a__		=			encoder_input_tokens.shape[1]
         a__		=			torch.arange(__SCREAMING_SNAKE_CASE     ,				device=encoder_input_tokens.device   )
         x += self.position_encoding(__SCREAMING_SNAKE_CASE   )

         a__		=			self.dropout_pre(__SCREAMING_SNAKE_CASE   )

         # inverted the attention mask
         a__		=			encoder_input_tokens.size()
         a__		=			self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         for lyr in self.encoders:
             a__		=			lyr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )[0]
         a__		=			self.layer_norm(__SCREAMING_SNAKE_CASE   )

         return self.dropout_post(__SCREAMING_SNAKE_CASE   ), encoder_inputs_mask


