


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()
SCREAMING_SNAKE_CASE			: str				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: Dict				   =					{
    "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",
}
SCREAMING_SNAKE_CASE			: Optional[int]				   =					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Dict:
    for attribute in key.split('.'					):
        _lowercase			:				Optional[int] 						=						getattr(lowerCamelCase_				,					lowerCamelCase_					)

    if weight_type is not None:
        _lowercase			:				Tuple 						=						getattr(lowerCamelCase_				,					lowerCamelCase_					).shape
    else:
        _lowercase			:				int 						=						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":
        _lowercase			:				Union[str, Any] 						=						value
    elif weight_type == "weight_g":
        _lowercase			:				str 						=						value
    elif weight_type == "weight_v":
        _lowercase			:				Optional[int] 						=						value
    elif weight_type == "bias":
        _lowercase			:				str 						=						value
    else:
        _lowercase			:				Union[str, Any] 						=						value

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



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Any:
    _lowercase			:				str 						=						[]
    _lowercase			:				List[Any] 						=						fairseq_model.state_dict()

    _lowercase			:				Dict 						=						hf_model.feature_extractor
    _lowercase			:				Tuple 						=						hf_model.adapter

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

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



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



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		Dict:
    _lowercase			:				Optional[Any] 						=						full_name.split('adaptor.'					)[-1]
    _lowercase			:				List[str] 						=						name.split('.'					)

    if items[1].isdigit():
        _lowercase			:				Any 						=						int(items[1]					)
    else:
        _lowercase			:				Optional[int] 						=						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.'''
                _lowercase			:				Dict 						=						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.'''
                _lowercase			:				List[Any] 						=						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.'''
                _lowercase			:				List[str] 						=						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.'''
                _lowercase			:				Any 						=						value
                logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''					)
    elif isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        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.'''
            _lowercase			:				List[str] 						=						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.'''
            _lowercase			:				str 						=						value
            logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''					)
    else:
        unused_weights.append(lowerCamelCase_					)



def 							UpperCamelCase_( lowerCamelCase_					)       ->		List[str]:
    _lowercase						,							_lowercase			:				List[str] 						=						emb.weight.shape
    _lowercase			:				Tuple 						=						nn.Linear(lowerCamelCase_				,					lowerCamelCase_				,					bias=lowerCamelCase_					)
    _lowercase			:				List[Any] 						=						emb.weight.data
    return lin_layer



@torch.no_grad()
def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_				,					)       ->		Union[str, Any]:
    _lowercase			:				Any 						=						WavaVecaConfig.from_pretrained(
        lowerCamelCase_				,					add_adapter=lowerCamelCase_				,					adapter_stride=lowerCamelCase_				,					adapter_kernel_size=lowerCamelCase_				,					use_auth_token=lowerCamelCase_				,					output_hidden_size=lowerCamelCase_				,					)
    _lowercase			:				int 						=						MBartConfig.from_pretrained(lowerCamelCase_					)

    # load model
    _lowercase						,							_lowercase						,							_lowercase			:				Dict 						=						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,
        }				,					)
    _lowercase			:				Dict 						=						model[0].eval()

    # load feature extractor
    _lowercase			:				Tuple 						=						WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_				,					use_auth_token=lowerCamelCase_					)

    # set weights for wav2vec2 encoder
    _lowercase			:				str 						=						WavaVecaModel(lowerCamelCase_					)

    recursively_load_weights_wavaveca(model.encoder				,					lowerCamelCase_					)

    # load decoder weights
    _lowercase			:				List[str] 						=						MBartForCausalLM(lowerCamelCase_					)
    _lowercase						,							_lowercase			:				Optional[int] 						=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()				,					strict=lowerCamelCase_					)
    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}'''					)

    _lowercase			:				Optional[Any] 						=						SpeechEncoderDecoderModel(encoder=lowerCamelCase_				,					decoder=lowerCamelCase_					)
    _lowercase			:				int 						=						False

    _lowercase			:				str 						=						MBartaaTokenizer(lowerCamelCase_					)
    tokenizer.save_pretrained(lowerCamelCase_					)

    _lowercase			:				Tuple 						=						hf_wavavec.config.to_dict()
    _lowercase			:				int 						=						tokenizer.pad_token_id
    _lowercase			:				Optional[Any] 						=						tokenizer.bos_token_id
    _lowercase			:				int 						=						tokenizer.eos_token_id
    _lowercase			:				Tuple 						=						'mbart50'
    _lowercase			:				Tuple 						=						'wav2vec2'

    _lowercase			:				Dict 						=						tokenizer.eos_token_id
    _lowercase			:				List[Any] 						=						25_0004
    _lowercase			:				int 						=						tokenizer.eos_token_id

    _lowercase			:				Any 						=						SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_					)

    hf_wavavec.save_pretrained(lowerCamelCase_					)
    feature_extractor.save_pretrained(lowerCamelCase_					)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE			: Union[str, 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=1024, type=int, help="encoder output dim")
 parser.add_argument("--start_token_id", default=250004, type=int, help="`decoder_start_token_id` of model config")

 SCREAMING_SNAKE_CASE			: Dict				   =					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'''



from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  ['''input_values''', '''padding_mask''']




						def __init__(   self       ,  _lowercase = 1       ,  _lowercase = 24_000       ,  _lowercase = 0.0       ,  _lowercase = None       ,  _lowercase = None       ,  **_lowercase       ,  ):

												"""simple docstring"""



												super().__init__(feature_size=_lowercase       ,  sampling_rate=_lowercase       ,  padding_value=_lowercase       ,  **_lowercase )
												_lowerCAmelCase    		=					chunk_length_s
												_lowerCAmelCase    		=					overlap




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.chunk_length_s is None:
																		return None
												else:
																		return int(self.chunk_length_s * self.sampling_rate )




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												if self.chunk_length_s is None or self.overlap is None:
																		return None
												else:
																		return max(1       ,  int((1.0 - self.overlap) * self.chunk_length ) )




						def __call__(   self       ,  _lowercase       ,  _lowercase = None       ,  _lowercase = False       ,  _lowercase = None       ,  _lowercase = None       ,  _lowercase = None       ,  ):

												"""simple docstring"""



												if sampling_rate is not None:
																		if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																								    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
																								    F' {self.sampling_rate} and not {sampling_rate}.' )
												else:
																		logger.warning(
																		    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
																		    """Failing to do so can result in silent errors that might be hard to debug.""" )

												if padding and truncation:
																		raise ValueError("""Both padding and truncation were set. Make sure you only set one.""" )
												elif padding is None:
																		# by default let's pad the inputs
																		_lowerCAmelCase    		=					True

												_lowerCAmelCase    		=					bool(
												    isinstance(_lowercase       ,  (list, tuple) ) and (isinstance(raw_audio[0]       ,  (np.ndarray, tuple, list) )) )

												if is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase       ,  dtype=np.floataa ).T for audio in raw_audio]
												elif not is_batched and not isinstance(_lowercase       ,  np.ndarray ):
																		_lowerCAmelCase    		=					np.asarray(_lowercase       ,  dtype=np.floataa )
												elif isinstance(_lowercase       ,  np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
																		_lowerCAmelCase    		=					raw_audio.astype(np.floataa )

												# always return batch
												if not is_batched:
																		_lowerCAmelCase    		=					[np.asarray(_lowercase ).T]

												# verify inputs are valid
												for idx, example in enumerate(_lowercase ):
																		if example.ndim > 2:
																								raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}' )
																		if self.feature_size == 1 and example.ndim != 1:
																								raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels' )
																		if self.feature_size == 2 and example.shape[-1] != 2:
																								raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels' )

												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					BatchFeature({"""input_values""": raw_audio} )
												if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
																		if truncation:
																								_lowerCAmelCase    		=					min(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.floor(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																		elif padding:
																								_lowerCAmelCase    		=					max(array.shape[0] for array in raw_audio )
																								_lowerCAmelCase    		=					int(np.ceil(max_length / self.chunk_stride ) )
																								_lowerCAmelCase    		=					(nb_step - 1) * self.chunk_stride + self.chunk_length
																								_lowerCAmelCase    		=					"""max_length"""
																		else:
																								_lowerCAmelCase    		=					input_values

        # normal padding on batch
												if padded_inputs is None:
																		_lowerCAmelCase    		=					self.pad(
																		    _lowercase       ,  max_length=_lowercase       ,  truncation=_lowercase       ,  padding=_lowercase       ,  return_attention_mask=_lowercase       ,  )
																		if padding:
																								_lowerCAmelCase    		=					padded_inputs.pop("""attention_mask""" )

												_lowerCAmelCase    		=					[]
												for example in padded_inputs.pop("""input_values""" ):
																		if self.feature_size == 1:
																								_lowerCAmelCase    		=					example[..., None]
																		input_values.append(example.T )

												_lowerCAmelCase    		=					input_values
												if return_tensors is not None:
																		_lowerCAmelCase    		=					padded_inputs.convert_to_tensors(_lowercase )

												return padded_inputs
