


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

SCREAMING_SNAKE_CASE						: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''',
    '''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''',
}
SCREAMING_SNAKE_CASE						:Any			=  [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]



def       _lowerCAmelCase    (				lowerCAmelCase_				:int				)->Tuple:
   '''simple docstring'''






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



def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:Any		,    lowerCAmelCase_				:int				)->str:
   '''simple docstring'''






   for attribute in key.split("."				):
      snake_case_            =       getattr(lowerCAmelCase_		,    lowerCAmelCase_				)

   snake_case_            =       None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(lowerCAmelCase_				):
         snake_case_            =       PARAM_MAPPING[full_name.split("."				)[-1]]
         snake_case_            =       "param"

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

      # let's reduce dimension
      snake_case_            =       value[0]
   else:
      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":
      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
   elif weight_type == "param":
      for attribute in hf_param_name.split("."				):
         snake_case_            =       getattr(lowerCAmelCase_		,    lowerCAmelCase_				)
      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       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:Any		,    lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:Optional[Any]		,    lowerCAmelCase_				:Any				)->int:
   '''simple docstring'''






   snake_case_            =       None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(lowerCAmelCase_				):
         snake_case_            =       PARAM_MAPPING[full_name.split("."				)[-1]]
         snake_case_            =       "param"

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

   snake_case_            =       value if "lm_head" in full_key else value[0]


SCREAMING_SNAKE_CASE						:List[str]			=  {
    '''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    (				lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Union[str, Any]		,    lowerCAmelCase_				:Union[str, Any]=None		,    lowerCAmelCase_				:Dict=None				)->Dict:
   '''simple docstring'''






   snake_case_            =       False
   for key, mapped_key in MAPPING.items():
      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]:
         snake_case_            =       True
         if "*" in mapped_key:
            snake_case_            =       name.split(lowerCAmelCase_				)[0].split("."				)[-2]
            snake_case_            =       mapped_key.replace("*"		,    lowerCAmelCase_				)
         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:
            # TODO: don't match quantizer.weight_proj
            snake_case_            =       "weight"
         else:
            snake_case_            =       None
         if hf_dict is not None:
            rename_dict(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         else:
            set_recursively(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         return is_used
   return is_used



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:str				)->Optional[int]:
   '''simple docstring'''






   snake_case_            =       []
   snake_case_            =       fairseq_model.state_dict()

   snake_case_            =       hf_model.wavaveca.feature_extractor

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

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



def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:Any		,    lowerCAmelCase_				: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:
         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.'''				)
         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.'''				)
         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.'''				)
         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.'''				)
         snake_case_            =       value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
   else:
      unused_weights.append(lowerCAmelCase_				)



@torch.no_grad()
def       _lowerCAmelCase    (				lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Any		,    lowerCAmelCase_				:Union[str, Any]=None		,    lowerCAmelCase_				:int=None		,    lowerCAmelCase_				:Optional[Any]=True		,    lowerCAmelCase_				:Dict=False				)->Tuple:
   '''simple docstring'''






   if config_path is not None:
      snake_case_            =       WavaVecaConfig.from_pretrained(lowerCAmelCase_				)
   else:
      snake_case_            =       WavaVecaConfig()

   if is_seq_class:
      snake_case_            =       read_txt_into_dict(lowerCAmelCase_				)
      snake_case_            =       idalabel
      snake_case_            =       WavaVecaForSequenceClassification(lowerCAmelCase_				)
      snake_case_            =       WavaVecaFeatureExtractor(
          feature_size=1		,    sampling_rate=16_000		,    padding_value=0		,    do_normalize=lowerCAmelCase_		,    return_attention_mask=lowerCAmelCase_		,    )
      feature_extractor.save_pretrained(lowerCAmelCase_				)

   elif is_finetuned:
      if dict_path:
         snake_case_            =       Dictionary.load(lowerCAmelCase_				)

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

         # fairseq has the <pad> and <s> switched
         snake_case_            =       0
         snake_case_            =       1
         with open(lowerCAmelCase_		,    "w"		,    encoding="utf-8"				) as vocab_handle:
            json.dump(lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       WavaVecaCTCTokenizer(
             lowerCAmelCase_		,    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=lowerCAmelCase_		,    )
         snake_case_            =       True if config.feat_extract_norm == "layer" else False
         snake_case_            =       WavaVecaFeatureExtractor(
             feature_size=1		,    sampling_rate=16_000		,    padding_value=0		,    do_normalize=lowerCAmelCase_		,    return_attention_mask=lowerCAmelCase_		,    )
         snake_case_            =       WavaVecaProcessor(feature_extractor=lowerCAmelCase_		,    tokenizer=lowerCAmelCase_				)
         processor.save_pretrained(lowerCAmelCase_				)

      snake_case_            =       WavaVecaForCTC(lowerCAmelCase_				)
   else:
      snake_case_            =       WavaVecaForPreTraining(lowerCAmelCase_				)

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

      snake_case_     ,     snake_case_     ,     snake_case_            =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,    task=lowerCAmelCase_				)

   snake_case_            =       model[0].eval()

   recursively_load_weights(lowerCAmelCase_		,    lowerCAmelCase_		,    not is_finetuned				)

   hf_wavavec.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						: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_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''',
   )
   SCREAMING_SNAKE_CASE						:int			=  parser.parse_args()

   SCREAMING_SNAKE_CASE						:Dict			=  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,
   )








from collections import deque

from .hash_table import HashTable


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							int     ,					*_lowerCAmelCase							:							List[Any]     ,					**_lowerCAmelCase							:							Any	) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							List[Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       deque([]	) if self.values[key] is None else self.values[key]
     self.values[key].appendleft(_lowerCAmelCase	)
     snake_case_            =       self.values[key]



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			List[Any]:




     """simple docstring"""
     return (
         sum(self.charge_factor - len(_lowerCAmelCase	) for slot in self.values	)
         / self.size_table
         * self.charge_factor
     )



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]=None	) ->			int:




     """simple docstring"""
     if not (
         len(self.values[key]	) == self.charge_factor and self.values.count(_lowerCAmelCase	) == 0
     ):
        return key
     return super()._collision_resolution(_lowerCAmelCase     ,					_lowerCAmelCase	)




