





'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCamelCase			:				Optional[Any] 			=					logging.get_logger(__name__)

lowerCamelCase			:				str 			=					{
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
lowerCamelCase			:				Optional[int] 			=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]


def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict    )				->      Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   ={}
     with open(SCREAMING_SNAKE_CASE_							,			'r'    ) as file:
          for line_number, line in enumerate(SCREAMING_SNAKE_CASE_    ):
               _SCREAMING_SNAKE_CASE							   =line.strip()
               if line:
                    _SCREAMING_SNAKE_CASE							   =line.split()
                    _SCREAMING_SNAKE_CASE							   =line_number
                    _SCREAMING_SNAKE_CASE							   =words[0]
                    _SCREAMING_SNAKE_CASE							   =value
     return result


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					str							,			_UpperCamelCase						:					Union[str, Any]    )				->      Optional[int]:
     """simple docstring"""




     for attribute in key.split('.'    ):
          _SCREAMING_SNAKE_CASE							   =getattr(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_    )

     _SCREAMING_SNAKE_CASE							   =None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(SCREAMING_SNAKE_CASE_    ):
               _SCREAMING_SNAKE_CASE							   =PARAM_MAPPING[full_name.split('.'    )[-1]]
               _SCREAMING_SNAKE_CASE							   ='param'

     if weight_type is not None and weight_type != "param":
          _SCREAMING_SNAKE_CASE							   =getattr(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_    ).shape
     elif weight_type is not None and weight_type == "param":
          _SCREAMING_SNAKE_CASE							   =hf_pointer
          for attribute in hf_param_name.split('.'    ):
               _SCREAMING_SNAKE_CASE							   =getattr(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_    )
          _SCREAMING_SNAKE_CASE							   =shape_pointer.shape

          # let's reduce dimension
          _SCREAMING_SNAKE_CASE							   =value[0]
     else:
          _SCREAMING_SNAKE_CASE							   =hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              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":
          _SCREAMING_SNAKE_CASE							   =value
     elif weight_type == "weight_g":
          _SCREAMING_SNAKE_CASE							   =value
     elif weight_type == "weight_v":
          _SCREAMING_SNAKE_CASE							   =value
     elif weight_type == "bias":
          _SCREAMING_SNAKE_CASE							   =value
     elif weight_type == "param":
          for attribute in hf_param_name.split('.'    ):
               _SCREAMING_SNAKE_CASE							   =getattr(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_    )
          _SCREAMING_SNAKE_CASE							   =value
     else:
          _SCREAMING_SNAKE_CASE							   =value

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					List[Any]    )				->      str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(SCREAMING_SNAKE_CASE_    ):
               _SCREAMING_SNAKE_CASE							   =PARAM_MAPPING[full_name.split('.'    )[-1]]
               _SCREAMING_SNAKE_CASE							   ='param'

     if weight_type is not None and weight_type != "param":
          _SCREAMING_SNAKE_CASE							   ='.'.join([key, weight_type]    )
     elif weight_type is not None and weight_type == "param":
          _SCREAMING_SNAKE_CASE							   ='.'.join([key, hf_param_name]    )
     else:
          _SCREAMING_SNAKE_CASE							   =key

     _SCREAMING_SNAKE_CASE							   =value if 'lm_head' in full_key else value[0]


lowerCamelCase			:				List[Any] 			=					{
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}


def 	_lowerCAmelCase (						_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Any							,			_UpperCamelCase						:					List[str]=None							,			_UpperCamelCase						:					Union[str, Any]=None    )				->      Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =False
     for key, mapped_key in MAPPING.items():
          _SCREAMING_SNAKE_CASE							   ='wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
          if key in name or key.split('w2v_model.'    )[-1] == name.split('.'    )[0]:
               _SCREAMING_SNAKE_CASE							   =True
               if "*" in mapped_key:
                    _SCREAMING_SNAKE_CASE							   =name.split(SCREAMING_SNAKE_CASE_    )[0].split('.'    )[-2]
                    _SCREAMING_SNAKE_CASE							   =mapped_key.replace('*'							,			SCREAMING_SNAKE_CASE_    )
               if "weight_g" in name:
                    _SCREAMING_SNAKE_CASE							   ='weight_g'
               elif "weight_v" in name:
                    _SCREAMING_SNAKE_CASE							   ='weight_v'
               elif "bias" in name:
                    _SCREAMING_SNAKE_CASE							   ='bias'
               elif "weight" in name:
                    # TODO: don't match quantizer.weight_proj
                    _SCREAMING_SNAKE_CASE							   ='weight'
               else:
                    _SCREAMING_SNAKE_CASE							   =None
               if hf_dict is not None:
                    rename_dict(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_    )
               else:
                    set_recursively(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_    )
               return is_used
     return is_used


def 	_lowerCAmelCase (						_UpperCamelCase						:					str							,			_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Any    )				->      str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[]
     _SCREAMING_SNAKE_CASE							   =fairseq_model.state_dict()

     _SCREAMING_SNAKE_CASE							   =hf_model.wavaveca.feature_extractor

     for name, value in fairseq_dict.items():
          _SCREAMING_SNAKE_CASE							   =False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			hf_model.config.feat_extract_norm == 'group'							,			)
               _SCREAMING_SNAKE_CASE							   =True
          else:
               _SCREAMING_SNAKE_CASE							   =load_wavaveca_layer(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_    )
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE_    )

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					str							,			_UpperCamelCase						:					Any    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =full_name.split('conv_layers.'    )[-1]
     _SCREAMING_SNAKE_CASE							   =name.split('.'    )
     _SCREAMING_SNAKE_CASE							   =int(items[0]    )
     _SCREAMING_SNAKE_CASE							   =int(items[1]    )

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."    )
               _SCREAMING_SNAKE_CASE							   =value
               logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."    )
               _SCREAMING_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:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."    )
               _SCREAMING_SNAKE_CASE							   =value
               logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f"{full_name} has size {value.shape}, but"
                        f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."    )
               _SCREAMING_SNAKE_CASE							   =value
               logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
     else:
          unused_weights.append(SCREAMING_SNAKE_CASE_    )


@torch.no_grad()
def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					List[str]=None							,			_UpperCamelCase						:					str=None							,			_UpperCamelCase						:					Tuple=True							,			_UpperCamelCase						:					Any=False    )				->      List[Any]:
     """simple docstring"""




     if config_path is not None:
          _SCREAMING_SNAKE_CASE							   =WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE_    )
     else:
          _SCREAMING_SNAKE_CASE							   =WavaVecaConfig()

     if is_seq_class:
          _SCREAMING_SNAKE_CASE							   =read_txt_into_dict(SCREAMING_SNAKE_CASE_    )
          _SCREAMING_SNAKE_CASE							   =idalabel
          _SCREAMING_SNAKE_CASE							   =WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE_    )
          _SCREAMING_SNAKE_CASE							   =WavaVecaFeatureExtractor(
              feature_size=1							,			sampling_rate=1_60_00							,			padding_value=0							,			do_normalize=SCREAMING_SNAKE_CASE_							,			return_attention_mask=SCREAMING_SNAKE_CASE_							,			)
          feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_    )

     elif is_finetuned:
          if dict_path:
               _SCREAMING_SNAKE_CASE							   =Dictionary.load(SCREAMING_SNAKE_CASE_    )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               _SCREAMING_SNAKE_CASE							   =target_dict.pad_index
               _SCREAMING_SNAKE_CASE							   =target_dict.bos_index
               _SCREAMING_SNAKE_CASE							   =target_dict.eos_index
               _SCREAMING_SNAKE_CASE							   =len(target_dict.symbols    )
               _SCREAMING_SNAKE_CASE							   =os.path.join(SCREAMING_SNAKE_CASE_							,			'vocab.json'    )
               if not os.path.isdir(SCREAMING_SNAKE_CASE_    ):
                    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE_    )    )
                    return
               os.makedirs(SCREAMING_SNAKE_CASE_							,			exist_ok=SCREAMING_SNAKE_CASE_    )
               _SCREAMING_SNAKE_CASE							   =target_dict.indices

               # fairseq has the <pad> and <s> switched
               _SCREAMING_SNAKE_CASE							   =0
               _SCREAMING_SNAKE_CASE							   =1
               with open(SCREAMING_SNAKE_CASE_							,			'w'							,			encoding='utf-8'    ) as vocab_handle:
                    json.dump(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_    )
               _SCREAMING_SNAKE_CASE							   =WavaVecaCTCTokenizer(
                   SCREAMING_SNAKE_CASE_							,			unk_token=target_dict.unk_word							,			pad_token=target_dict.pad_word							,			bos_token=target_dict.bos_word							,			eos_token=target_dict.eos_word							,			word_delimiter_token='|'							,			do_lower_case=SCREAMING_SNAKE_CASE_							,			)
               _SCREAMING_SNAKE_CASE							   =True if config.feat_extract_norm == 'layer' else False
               _SCREAMING_SNAKE_CASE							   =WavaVecaFeatureExtractor(
                   feature_size=1							,			sampling_rate=1_60_00							,			padding_value=0							,			do_normalize=SCREAMING_SNAKE_CASE_							,			return_attention_mask=SCREAMING_SNAKE_CASE_							,			)
               _SCREAMING_SNAKE_CASE							   =WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_							,			tokenizer=SCREAMING_SNAKE_CASE_    )
               processor.save_pretrained(SCREAMING_SNAKE_CASE_    )

          _SCREAMING_SNAKE_CASE							   =WavaVecaForCTC(SCREAMING_SNAKE_CASE_    )
     else:
          _SCREAMING_SNAKE_CASE							   =WavaVecaForPreTraining(SCREAMING_SNAKE_CASE_    )

     if is_finetuned or is_seq_class:
          _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]							,			arg_overrides={'data': '/'.join(dict_path.split('/'    )[:-1]    )}    )
     else:
          _SCREAMING_SNAKE_CASE							   =argparse.Namespace(task='audio_pretraining'    )
          _SCREAMING_SNAKE_CASE							   =fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE_    )

          _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,			task=SCREAMING_SNAKE_CASE_    )

     _SCREAMING_SNAKE_CASE							   =model[0].eval()

     recursively_load_weights(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			not is_finetuned    )

     hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
     lowerCamelCase			:				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_path", default=None, type=str, help="Path to hf config.json of model to convert")
     parser.add_argument(
         "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
     )
     parser.add_argument(
         "--is_seq_class",
         action="store_true",
         help="Whether the model to convert is a fine-tuned sequence classification model or not",
     )
     lowerCamelCase			:				Optional[Any] 			=					parser.parse_args()

     lowerCamelCase			:				Optional[Any] 			=					not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )



'''simple docstring'''
from ..utils import DummyObject, requires_backends







class 							A__   (	metaclass=A__  ):
  A__					=    ['note_seq']





  def __init__(		self				:     List[str]							, *_a				:     Any							, **_a				:     Dict			)     ->   Optional[Any]:





       '''simple docstring'''






       requires_backends(self							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     Any							, *_a				:     str							, **_a				:     List[Any]			)     ->   List[str]:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     int							, *_a				:     Optional[Any]							, **_a				:     Optional[int]			)     ->   Tuple:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)
