
"""simple docstring"""

import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE_            =    logging.getLogger(__name__)


SCREAMING_SNAKE_CASE_            =    list(MODEL_WITH_LM_HEAD_MAPPING.keys())
SCREAMING_SNAKE_CASE_            =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class   _UpperCAmelCase				:
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={
              "help": (
                  "The model checkpoint for weights initialization. Leave None if you want to train a model from"
                  " scratch."
              )
          }  ,      )
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowerCAmelCase				)}  ,      )
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,      )

@dataclass
class   _UpperCAmelCase				:
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "The input training data file (a text file)."}				)
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={
              "help": (
                  "The input training data files (multiple files in glob format). "
                  "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
              )
          }  ,      )
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}  ,      )
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "An optional input train ref data file for whole word mask in Chinese."}  ,      )
      __SCREAMING_SNAKE_CASE :				Optional[str]				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."}  ,      )
      __SCREAMING_SNAKE_CASE :				bool				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}  ,      )

      __SCREAMING_SNAKE_CASE :				bool				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "Train with masked-language modeling loss instead of language modeling."}				)
      __SCREAMING_SNAKE_CASE :				bool				   =						field(default=__lowerCAmelCase  ,      metadata={"help": "Whether ot not to use whole word mask."}				)
      __SCREAMING_SNAKE_CASE :				float				   =						field(
          default=0.15  ,      metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}				)
      __SCREAMING_SNAKE_CASE :				float				   =						field(
          default=1 / 6  ,      metadata={
              "help": (
                  "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
                  " modeling."
              )
          }  ,      )
      __SCREAMING_SNAKE_CASE :				int				   =						field(
          default=5  ,      metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}				)

      __SCREAMING_SNAKE_CASE :				int				   =						field(
          default=-1  ,      metadata={
              "help": (
                  "Optional input sequence length after tokenization."
                  "The training dataset will be truncated in block of this size for training."
                  "Default to the model max input length for single sentence inputs (take into account special tokens)."
              )
          }  ,      )
      __SCREAMING_SNAKE_CASE :				bool				   =						field(
          default=__lowerCAmelCase  ,      metadata={"help": "Overwrite the cached training and evaluation sets"}				)




def      lowercase__    (       lowerCAmelCase					:       DataTrainingArguments			,					lowerCAmelCase					:       PreTrainedTokenizer			,					lowerCAmelCase					:       bool = False			,					lowerCAmelCase					:       Optional[str] = None			,					)			->   List[str]:






    """simple docstring"""




    def _dataset(lowerCAmelCase					:       Dict			,					lowerCAmelCase					:       Any=None			):
        if args.line_by_line:
            if ref_path is not None:
                if not args.whole_word_mask or not args.mlm:
                    raise ValueError('You need to set world whole masking and mlm to True for Chinese Whole Word Mask'			)
                return LineByLineWithRefDataset(
                    tokenizer=_UpperCamelCase			,					file_path=_UpperCamelCase			,					block_size=args.block_size			,					ref_path=_UpperCamelCase			,					)

            return LineByLineTextDataset(tokenizer=_UpperCamelCase			,					file_path=_UpperCamelCase			,					block_size=args.block_size			)
        else:
            return TextDataset(
                tokenizer=_UpperCamelCase			,					file_path=_UpperCamelCase			,					block_size=args.block_size			,					overwrite_cache=args.overwrite_cache			,					cache_dir=_UpperCamelCase			,					)

    if evaluate:
        return _dataset(args.eval_data_file			,					args.eval_ref_file			)
    elif args.train_data_files:
        return ConcatDataset([_dataset(_UpperCamelCase			) for f in glob(args.train_data_files			)]			)
    else:
        return _dataset(args.train_data_file			,					args.train_ref_file			)




def      lowercase__    (       )			->   Any:






    """simple docstring"""




    UpperCAmelCase          =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
    UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       parser.parse_args_into_dataclasses()

    if data_args.eval_data_file is None and training_args.do_eval:
        raise ValueError(
            'Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '
            'or remove the --do_eval argument.'			)
    if (
        os.path.exists(training_args.output_dir			)
        and os.listdir(training_args.output_dir			)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
            ' --overwrite_output_dir to overcome.'			)

    # Setup logging
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,					datefmt='%m/%d/%Y %H:%M:%S'			,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,					)
    logger.warning(
        'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'			,					training_args.local_rank			,					training_args.device			,					training_args.n_gpu			,					bool(training_args.local_rank != -1			)			,					training_args.fpaa			,					)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank			):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info('Training/evaluation parameters %s'			,					_UpperCamelCase			)

    # Set seed
    set_seed(training_args.seed			)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    if model_args.config_name:
        UpperCAmelCase          =       AutoConfig.from_pretrained(model_args.config_name			,					cache_dir=model_args.cache_dir			)
    elif model_args.model_name_or_path:
        UpperCAmelCase          =       AutoConfig.from_pretrained(model_args.model_name_or_path			,					cache_dir=model_args.cache_dir			)
    else:
        UpperCAmelCase          =       CONFIG_MAPPING[model_args.model_type]()
        logger.warning('You are instantiating a new config instance from scratch.'			)

    if model_args.tokenizer_name:
        UpperCAmelCase          =       AutoTokenizer.from_pretrained(model_args.tokenizer_name			,					cache_dir=model_args.cache_dir			)
    elif model_args.model_name_or_path:
        UpperCAmelCase          =       AutoTokenizer.from_pretrained(model_args.model_name_or_path			,					cache_dir=model_args.cache_dir			)
    else:
        raise ValueError(
            'You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'
            ' script, save it,and load it from here, using --tokenizer_name'			)

    if model_args.model_name_or_path:
        UpperCAmelCase          =       AutoModelWithLMHead.from_pretrained(
            model_args.model_name_or_path			,					from_tf=bool('.ckpt' in model_args.model_name_or_path			)			,					config=_UpperCamelCase			,					cache_dir=model_args.cache_dir			,					)
    else:
        logger.info('Training new model from scratch'			)
        UpperCAmelCase          =       AutoModelWithLMHead.from_config(_UpperCamelCase			)

    model.resize_token_embeddings(len(_UpperCamelCase			)			)

    if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
        raise ValueError(
            'BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'
            '--mlm flag (masked language modeling).'			)

    if data_args.block_size <= 0:
        UpperCAmelCase          =       tokenizer.max_len
        # Our input block size will be the max possible for the model
    else:
        UpperCAmelCase          =       min(data_args.block_size			,					tokenizer.max_len			)

    # Get datasets

    UpperCAmelCase          =       (
        get_dataset(_UpperCamelCase			,					tokenizer=_UpperCamelCase			,					cache_dir=model_args.cache_dir			) if training_args.do_train else None
    )
    UpperCAmelCase          =       (
        get_dataset(_UpperCamelCase			,					tokenizer=_UpperCamelCase			,					evaluate=_UpperCamelCase			,					cache_dir=model_args.cache_dir			)
        if training_args.do_eval
        else None
    )
    if config.model_type == "xlnet":
        UpperCAmelCase          =       DataCollatorForPermutationLanguageModeling(
            tokenizer=_UpperCamelCase			,					plm_probability=data_args.plm_probability			,					max_span_length=data_args.max_span_length			,					)
    else:
        if data_args.mlm and data_args.whole_word_mask:
            UpperCAmelCase          =       DataCollatorForWholeWordMask(
                tokenizer=_UpperCamelCase			,					mlm_probability=data_args.mlm_probability			)
        else:
            UpperCAmelCase          =       DataCollatorForLanguageModeling(
                tokenizer=_UpperCamelCase			,					mlm=data_args.mlm			,					mlm_probability=data_args.mlm_probability			)

    # Initialize our Trainer
    UpperCAmelCase          =       Trainer(
        model=_UpperCamelCase			,					args=_UpperCamelCase			,					data_collator=_UpperCamelCase			,					train_dataset=_UpperCamelCase			,					eval_dataset=_UpperCamelCase			,					prediction_loss_only=_UpperCamelCase			,					)

    # Training
    if training_args.do_train:
        UpperCAmelCase          =       (
            model_args.model_name_or_path
            if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path			)
            else None
        )
        trainer.train(model_path=_UpperCamelCase			)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
    UpperCAmelCase          =       {}
    if training_args.do_eval:
        logger.info('*** Evaluate ***'			)

        UpperCAmelCase          =       trainer.evaluate()

        UpperCAmelCase          =       math.exp(eval_output['eval_loss']			)
        UpperCAmelCase          =       {'perplexity': perplexity}

        UpperCAmelCase          =       os.path.join(training_args.output_dir			,					'eval_results_lm.txt'			)
        if trainer.is_world_master():
            with open(_UpperCamelCase			,					'w'			) as writer:
                logger.info('***** Eval results *****'			)
                for key in sorted(result.keys()			):
                    logger.info('  %s = %s'			,					_UpperCamelCase			,					str(result[key]			)			)
                    writer.write('%s = %s\n' % (key, str(result[key]			))			)

        results.update(_UpperCamelCase			)

    return results




def      lowercase__    (       lowerCAmelCase					:       Optional[Any]			)			->   Optional[int]:






    """simple docstring"""




    main()


if __name__ == "__main__":
   main()









'''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()
UpperCamelCase_		:     str          =		logging.get_logger(__name__)

UpperCamelCase_		:     Optional[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''',
}
UpperCamelCase_		:     int          =		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def       __a      (  _UpperCamelCase:       int   ,		_UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Any   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Optional[int]       )		->					int:

     """simple docstring"""




     for attribute in key.split("."       ):
          _snake_case				= getattr(_UpperCamelCase   ,		_UpperCamelCase       )

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

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





def       __a      (  _UpperCamelCase:       int   ,		_UpperCamelCase:       Tuple       )		->					str:

     """simple docstring"""




     _snake_case				= []
     _snake_case				= fairseq_model.state_dict()

     _snake_case				= hf_model.feature_extractor
     _snake_case				= hf_model.adapter

     for name, value in fairseq_dict.items():
          _snake_case				= False
          if "conv_layers" in name:
               load_conv_layer(
                   _UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		hf_model.config.feat_extract_norm == "group"   ,		)
               _snake_case				= True
          elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]       ):
               load_adapter(_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase       )
               _snake_case				= True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
                         _snake_case				= True
                         if "*" in mapped_key:
                              _snake_case				= name.split(_UpperCamelCase       )[0].split("."       )[-2]
                              _snake_case				= mapped_key.replace("*"   ,		_UpperCamelCase       )
                         if "weight_g" in name:
                              _snake_case				= "weight_g"
                         elif "weight_v" in name:
                              _snake_case				= "weight_v"
                         elif "bias" in name:
                              _snake_case				= "bias"
                         elif "weight" in name:
                              _snake_case				= "weight"
                         else:
                              _snake_case				= 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       __a      (  _UpperCamelCase:       Dict   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       Optional[Any]   ,		_UpperCamelCase:       Optional[Any]       )		->					Dict:

     """simple docstring"""




     _snake_case				= full_name.split("conv_layers."       )[-1]
     _snake_case				= name.split("."       )
     _snake_case				= int(items[0]       )
     _snake_case				= 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."""
               )
               _snake_case				= 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."""
               )
               _snake_case				= 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."
               )
               _snake_case				= 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."""
               )
               _snake_case				= value
               logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
     else:
          unused_weights.append(_UpperCamelCase       )





def       __a      (  _UpperCamelCase:       Dict   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       Tuple       )		->					Any:

     """simple docstring"""




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

     if items[1].isdigit():
          _snake_case				= int(items[1]       )
     else:
          _snake_case				= 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."""
                    _snake_case				= 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."""
                    _snake_case				= 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."""
                    _snake_case				= 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."""
                    _snake_case				= 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."""
               _snake_case				= 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."""
               _snake_case				= value
               logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
     else:
          unused_weights.append(_UpperCamelCase       )





def       __a      (  _UpperCamelCase:       Tuple       )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				,   _snake_case				= emb.weight.shape
     _snake_case				= nn.Linear(_UpperCamelCase   ,		_UpperCamelCase   ,		bias=_UpperCamelCase       )
     _snake_case				= emb.weight.data
     return lin_layer





@torch.no_grad()
def       __a      (  _UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       Any   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Tuple   ,		)		->					List[str]:

     """simple docstring"""




     _snake_case				= WavaVecaConfig.from_pretrained(
         _UpperCamelCase   ,		add_adapter=_UpperCamelCase   ,		adapter_stride=_UpperCamelCase   ,		adapter_kernel_size=_UpperCamelCase   ,		use_auth_token=_UpperCamelCase   ,		output_hidden_size=_UpperCamelCase   ,		)
     _snake_case				= MBartConfig.from_pretrained(_UpperCamelCase       )

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

     # load feature extractor
     _snake_case				= WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase   ,		use_auth_token=_UpperCamelCase       )

     # set weights for wav2vec2 encoder
     _snake_case				= WavaVecaModel(_UpperCamelCase       )

     recursively_load_weights_wavaveca(model.encoder   ,		_UpperCamelCase       )

     # load decoder weights
     _snake_case				= MBartForCausalLM(_UpperCamelCase       )
     _snake_case				,   _snake_case				= 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}"""       )

     _snake_case				= SpeechEncoderDecoderModel(encoder=_UpperCamelCase   ,		decoder=_UpperCamelCase       )
     _snake_case				= False

     _snake_case				= MBartaaTokenizer(_UpperCamelCase       )
     tokenizer.save_pretrained(_UpperCamelCase       )

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

     _snake_case				= tokenizer.eos_token_id
     _snake_case				= 250_004
     _snake_case				= tokenizer.eos_token_id

     _snake_case				= SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase       )

     hf_wavavec.save_pretrained(_UpperCamelCase       )
     feature_extractor.save_pretrained(_UpperCamelCase       )


if __name__ == "__main__":
   UpperCamelCase_		:     str          =		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''')

   UpperCamelCase_		:     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,
   )


