




import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
  import transformers



def   lowerCAmelCase						(						_lowerCAmelCase							:      int		):




 """simple docstring"""







 random.seed(__SCREAMING_SNAKE_CASE		)
 np.random.seed(__SCREAMING_SNAKE_CASE		)
 torch.manual_seed(__SCREAMING_SNAKE_CASE		)
 torch.cuda.manual_seed_all(__SCREAMING_SNAKE_CASE		)
 # ^^ safe to call this function even if cuda is not available


class 	_UpperCamelCase					:
      def __init__(	self      :Optional[Any]       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :Optional[int] = 0.99_99       ,					lowerCamelCase      :Tuple = 0.0       ,					lowerCamelCase      :Optional[int] = 0       ,					lowerCamelCase      :Dict = False       ,					lowerCamelCase      :Optional[Any] = 1.0       ,					lowerCamelCase      :str = 2 / 3       ,					lowerCamelCase      :int = None       ,					lowerCamelCase      :str = None       ,					**lowerCamelCase      :List[str]       ,					)   ->	List[Any]:


       if isinstance(__UpperCamelCase       ,					torch.nn.Module					):
        UpperCAmelCase__							       =  (
            'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
            'Please pass the parameters of the module instead.'
        )
        deprecate(
            "passing a `torch.nn.Module` to `ExponentialMovingAverage`"       ,					"1.0.0"       ,					__UpperCamelCase       ,					standard_warn=__UpperCamelCase       ,					)
        UpperCAmelCase__							       =  parameters.parameters()

        # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
        UpperCAmelCase__							       =  True

       if kwargs.get("max_value"       ,					__UpperCamelCase					) is not None:
        UpperCAmelCase__							       =  'The `max_value` argument is deprecated. Please use `decay` instead.'
        deprecate("max_value"       ,					"1.0.0"       ,					__UpperCamelCase       ,					standard_warn=__UpperCamelCase					)
        UpperCAmelCase__							       =  kwargs['max_value']

       if kwargs.get("min_value"       ,					__UpperCamelCase					) is not None:
        UpperCAmelCase__							       =  'The `min_value` argument is deprecated. Please use `min_decay` instead.'
        deprecate("min_value"       ,					"1.0.0"       ,					__UpperCamelCase       ,					standard_warn=__UpperCamelCase					)
        UpperCAmelCase__							       =  kwargs['min_value']

       UpperCAmelCase__							       =  list(__UpperCamelCase					)
       UpperCAmelCase__							       =  [p.clone().detach() for p in parameters]

       if kwargs.get("device"       ,					__UpperCamelCase					) is not None:
        UpperCAmelCase__							       =  'The `device` argument is deprecated. Please use `to` instead.'
        deprecate("device"       ,					"1.0.0"       ,					__UpperCamelCase       ,					standard_warn=__UpperCamelCase					)
        self.to(device=kwargs["device"]					)

       UpperCAmelCase__							       =  None

       UpperCAmelCase__							       =  decay
       UpperCAmelCase__							       =  min_decay
       UpperCAmelCase__							       =  update_after_step
       UpperCAmelCase__							       =  use_ema_warmup
       UpperCAmelCase__							       =  inv_gamma
       UpperCAmelCase__							       =  power
       UpperCAmelCase__							       =  0
       UpperCAmelCase__							       =  None  # set in `step()`

       UpperCAmelCase__							       =  model_cls
       UpperCAmelCase__							       =  model_config
      @classmethod
      def 		UpperCAmelCase_  (	cls      :int       ,					lowerCamelCase      :int       ,					lowerCamelCase      :List[str]					)   ->	"EMAModel":
       UpperCAmelCase__							       =  model_cls.load_config(__UpperCamelCase       ,					return_unused_kwargs=__UpperCamelCase					)
       UpperCAmelCase__							       =  model_cls.from_pretrained(__UpperCamelCase					)

       UpperCAmelCase__							       =  cls(model.parameters()       ,					model_cls=__UpperCamelCase       ,					model_config=model.config					)

       ema_model.load_state_dict(__UpperCamelCase					)
       return ema_model
      def 		UpperCAmelCase_  (	self      :int       ,					lowerCamelCase      :Tuple					)   ->	Union[str, Any]:


       if self.model_cls is None:
        raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."					)

       if self.model_config is None:
        raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."					)

       UpperCAmelCase__							       =  self.model_cls.from_config(self.model_config					)
       UpperCAmelCase__							       =  self.state_dict()
       state_dict.pop("shadow_params"       ,					__UpperCamelCase					)

       model.register_to_config(**__UpperCamelCase					)
       self.copy_to(model.parameters()					)
       model.save_pretrained(__UpperCamelCase					)
      def 		UpperCAmelCase_  (	self      :int       ,					lowerCamelCase      :Any					)   ->	float:
       UpperCAmelCase__							       =  max(0       ,					optimization_step - self.update_after_step - 1					)

       if step <= 0:
        return 0.0

       if self.use_ema_warmup:
        UpperCAmelCase__							       =  1 - (1 + step / self.inv_gamma) ** -self.power
       else:
        UpperCAmelCase__							       =  (1 + step) / (10 + step)

       UpperCAmelCase__							       =  min(__UpperCamelCase       ,					self.decay					)
       # make sure decay is not smaller than min_decay
       UpperCAmelCase__							       =  max(__UpperCamelCase       ,					self.min_decay					)
       return cur_decay_value
      @torch.no_grad()
      def 		UpperCAmelCase_  (	self      :Tuple       ,					lowerCamelCase      :Dict					)   ->	Optional[Any]:


       if isinstance(__UpperCamelCase       ,					torch.nn.Module					):
        UpperCAmelCase__							       =  (
            'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
            'Please pass the parameters of the module instead.'
        )
        deprecate(
            "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"       ,					"1.0.0"       ,					__UpperCamelCase       ,					standard_warn=__UpperCamelCase       ,					)
        UpperCAmelCase__							       =  parameters.parameters()

       UpperCAmelCase__							       =  list(__UpperCamelCase					)

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       UpperCAmelCase__							       =  self.get_decay(self.optimization_step					)
       UpperCAmelCase__							       =  decay
       UpperCAmelCase__							       =  1 - decay

       UpperCAmelCase__							       =  contextlib.nullcontext
       if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
        import deepspeed

       for s_param, param in zip(self.shadow_params       ,					__UpperCamelCase					):
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
         UpperCAmelCase__							       =  deepspeed.zero.GatheredParameters(__UpperCamelCase       ,					modifier_rank=__UpperCamelCase					)

        with context_manager():
         if param.requires_grad:
          s_param.sub_(one_minus_decay * (s_param - param)					)
         else:
          s_param.copy_(__UpperCamelCase					)
      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :Tuple					)   ->	None:
       UpperCAmelCase__							       =  list(__UpperCamelCase					)
       for s_param, param in zip(self.shadow_params       ,					__UpperCamelCase					):
        param.data.copy_(s_param.to(param.device					).data					)
      def 		UpperCAmelCase_  (	self      :Optional[int]       ,					lowerCamelCase      :Tuple=None       ,					lowerCamelCase      :List[Any]=None					)   ->	None:
       UpperCAmelCase__							       =  [
           p.to(device=__UpperCamelCase       ,					dtype=__UpperCamelCase					) if p.is_floating_point() else p.to(device=__UpperCamelCase					)
           for p in self.shadow_params
       ]
      def 		UpperCAmelCase_  (	self      :str					)   ->	dict:
       return {
           "decay": self.decay,
           "min_decay": self.min_decay,
           "optimization_step": self.optimization_step,
           "update_after_step": self.update_after_step,
           "use_ema_warmup": self.use_ema_warmup,
           "inv_gamma": self.inv_gamma,
           "power": self.power,
           "shadow_params": self.shadow_params,
       }
      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :Optional[int]					)   ->	None:
       UpperCAmelCase__							       =  [param.detach().cpu().clone() for param in parameters]
      def 		UpperCAmelCase_  (	self      :Dict       ,					lowerCamelCase      :Optional[Any]					)   ->	None:


       if self.temp_stored_params is None:
        raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"					)
       for c_param, param in zip(self.temp_stored_params       ,					__UpperCamelCase					):
        param.data.copy_(c_param.data					)

       # Better memory-wise.
       UpperCAmelCase__							       =  None







      def 		UpperCAmelCase_  (	self      :Optional[int]       ,					lowerCamelCase      :Any					)   ->	None:
       UpperCAmelCase__							       =  copy.deepcopy(__UpperCamelCase					)

       UpperCAmelCase__							       =  state_dict.get("decay"       ,					self.decay					)
       if self.decay < 0.0 or self.decay > 1.0:
        raise ValueError("Decay must be between 0 and 1"					)

       UpperCAmelCase__							       =  state_dict.get("min_decay"       ,					self.min_decay					)
       if not isinstance(self.min_decay       ,					__UpperCamelCase					):
        raise ValueError("Invalid min_decay"					)

       UpperCAmelCase__							       =  state_dict.get("optimization_step"       ,					self.optimization_step					)
       if not isinstance(self.optimization_step       ,					__UpperCamelCase					):
        raise ValueError("Invalid optimization_step"					)

       UpperCAmelCase__							       =  state_dict.get("update_after_step"       ,					self.update_after_step					)
       if not isinstance(self.update_after_step       ,					__UpperCamelCase					):
        raise ValueError("Invalid update_after_step"					)

       UpperCAmelCase__							       =  state_dict.get("use_ema_warmup"       ,					self.use_ema_warmup					)
       if not isinstance(self.use_ema_warmup       ,					__UpperCamelCase					):
        raise ValueError("Invalid use_ema_warmup"					)

       UpperCAmelCase__							       =  state_dict.get("inv_gamma"       ,					self.inv_gamma					)
       if not isinstance(self.inv_gamma       ,					(float, int)					):
        raise ValueError("Invalid inv_gamma"					)

       UpperCAmelCase__							       =  state_dict.get("power"       ,					self.power					)
       if not isinstance(self.power       ,					(float, int)					):
        raise ValueError("Invalid power"					)

       UpperCAmelCase__							       =  state_dict.get("shadow_params"       ,					__UpperCamelCase					)
       if shadow_params is not None:
        UpperCAmelCase__							       =  shadow_params
        if not isinstance(self.shadow_params       ,					__UpperCamelCase					):
         raise ValueError("shadow_params must be a list"					)
        if not all(isinstance(__UpperCamelCase       ,					torch.Tensor					) for p in self.shadow_params					):
         raise ValueError("shadow_params must all be Tensors"					)






"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    	={
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     List[str]      ):
     for attribute in key.split('.'      ):
          lowercase_	:				Tuple													=	getattr(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     if weight_type is not None:
          lowercase_	:				List[Any]													=	getattr(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      ).shape
     else:
          lowercase_	:				str													=	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_	:				Optional[Any]													=	value
     elif weight_type == "weight_g":
          lowercase_	:				Optional[Any]													=	value
     elif weight_type == "weight_v":
          lowercase_	:				Optional[Any]													=	value
     elif weight_type == "bias":
          lowercase_	:				Union[str, Any]													=	value
     else:
          lowercase_	:				Any													=	value

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Tuple     ,			__SCREAMING_SNAKE_CASE							:     int     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]      ):
     lowercase_	:				Optional[int]													=	[]
     lowercase_	:				Optional[int]													=	fairseq_model.state_dict()

     lowercase_	:				List[str]													=	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase_	:				List[str]													=	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'     ,			)
               lowercase_	:				str													=	True
          else:
               for key, mapped_key in MAPPING.items():
                    lowercase_	:				int													=	'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

                    if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
                         lowercase_	:				str													=	True
                         if "*" in mapped_key:
                              lowercase_	:				int													=	name.split(__SCREAMING_SNAKE_CASE      )[0].split('.'      )[-2]
                              lowercase_	:				Optional[Any]													=	mapped_key.replace('*'     ,			__SCREAMING_SNAKE_CASE      )
                         if "weight_g" in name:
                              lowercase_	:				Any													=	'weight_g'
                         elif "weight_v" in name:
                              lowercase_	:				Tuple													=	'weight_v'
                         elif "weight" in name:
                              lowercase_	:				int													=	'weight'
                         elif "bias" in name:
                              lowercase_	:				List[Any]													=	'bias'
                         else:
                              lowercase_	:				Optional[Any]													=	None
                         set_recursively(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
                    continue
          if not is_used:
               unused_weights.append(__SCREAMING_SNAKE_CASE      )

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



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     str     ,			__SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     str      ):
     lowercase_	:				Dict													=	full_name.split('conv_layers.'      )[-1]
     lowercase_	:				int													=	name.split('.'      )
     lowercase_	:				Any													=	int(items[0]      )
     lowercase_	:				Tuple													=	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_	:				List[str]													=	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_	:				List[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_	:				Optional[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_	:				List[str]													=	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      )



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     int      ):
     lowercase_	:				str													=	SEWConfig()
     if is_finetuned:
          lowercase_	:				List[Any]													=	model.wav_encoder.wav_model.cfg
     else:
          lowercase_	:				Tuple													=	model.cfg

     lowercase_	:				Any													=	fs_config.conv_bias
     lowercase_	:				Optional[Any]													=	eval(fs_config.conv_feature_layers      )
     lowercase_	:				int													=	[x[0] for x in conv_layers]
     lowercase_	:				Any													=	[x[1] for x in conv_layers]
     lowercase_	:				Optional[Any]													=	[x[2] for x in conv_layers]
     lowercase_	:				Tuple													=	'gelu'
     lowercase_	:				str													=	'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     lowercase_	:				int													=	0.0
     lowercase_	:				Any													=	fs_config.activation_fn.name
     lowercase_	:				Tuple													=	fs_config.encoder_embed_dim
     lowercase_	:				int													=	0.02
     lowercase_	:				Union[str, Any]													=	fs_config.encoder_ffn_embed_dim
     lowercase_	:				Tuple													=	1E-5
     lowercase_	:				Union[str, Any]													=	fs_config.encoder_layerdrop
     lowercase_	:				Tuple													=	fs_config.encoder_attention_heads
     lowercase_	:				List[str]													=	fs_config.conv_pos_groups
     lowercase_	:				Union[str, Any]													=	fs_config.conv_pos
     lowercase_	:				str													=	len(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	fs_config.encoder_layers
     lowercase_	:				str													=	fs_config.squeeze_factor

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          lowercase_	:				Dict													=	model.cfg
          lowercase_	:				Dict													=	fs_config.final_dropout
          lowercase_	:				Dict													=	fs_config.layerdrop
     lowercase_	:				Optional[int]													=	fs_config.activation_dropout
     lowercase_	:				Any													=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     lowercase_	:				List[Any]													=	fs_config.attention_dropout
     lowercase_	:				Tuple													=	fs_config.dropout_input
     lowercase_	:				List[Any]													=	fs_config.dropout
     lowercase_	:				Any													=	fs_config.mask_channel_length
     lowercase_	:				str													=	fs_config.mask_channel_prob
     lowercase_	:				Optional[Any]													=	fs_config.mask_length
     lowercase_	:				Tuple													=	fs_config.mask_prob

     lowercase_	:				List[Any]													=	'Wav2Vec2FeatureExtractor'
     lowercase_	:				Union[str, Any]													=	'Wav2Vec2CTCTokenizer'

     return config



@torch.no_grad()
def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,			__SCREAMING_SNAKE_CASE							:     int=None     ,			__SCREAMING_SNAKE_CASE							:     Any=None     ,			__SCREAMING_SNAKE_CASE							:     Tuple=True      ):

     if is_finetuned:
          lowercase_							,  lowercase_							,  lowercase_	:				Optional[int]													=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]     ,			arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
     else:
          lowercase_							,  lowercase_							,  lowercase_	:				Union[str, Any]													=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

     if config_path is not None:
          lowercase_	:				List[str]													=	SEWConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )
     else:
          lowercase_	:				Tuple													=	convert_config(model[0]     ,			__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	model[0].eval()

     lowercase_	:				List[Any]													=	True if config.feat_extract_norm == 'layer' else False
     lowercase_	:				Optional[Any]													=	WavaVecaFeatureExtractor(
         feature_size=1     ,			sampling_rate=1_60_00     ,			padding_value=0     ,			do_normalize=__SCREAMING_SNAKE_CASE     ,			return_attention_mask=__SCREAMING_SNAKE_CASE     ,			)

     if is_finetuned:
          if dict_path:
               lowercase_	:				Dict													=	Dictionary.load(__SCREAMING_SNAKE_CASE      )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               lowercase_	:				str													=	target_dict.pad_index
               lowercase_	:				Union[str, Any]													=	target_dict.bos_index
               lowercase_	:				Tuple													=	target_dict.pad_index
               lowercase_	:				List[Any]													=	target_dict.bos_index
               lowercase_	:				Any													=	target_dict.eos_index
               lowercase_	:				str													=	len(target_dict.symbols      )
               lowercase_	:				Optional[Any]													=	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      )
               with open(__SCREAMING_SNAKE_CASE     ,			'w'     ,			encoding='utf-8'      ) as vocab_handle:
                    json.dump(target_dict.indices     ,			__SCREAMING_SNAKE_CASE      )
               lowercase_	:				Union[str, Any]													=	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     ,			)
               lowercase_	:				Tuple													=	WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE     ,			tokenizer=__SCREAMING_SNAKE_CASE      )
               processor.save_pretrained(__SCREAMING_SNAKE_CASE      )

          lowercase_	:				List[str]													=	SEWForCTC(__SCREAMING_SNAKE_CASE      )
     else:
          lowercase_	:				Any													=	SEWModel(__SCREAMING_SNAKE_CASE      )
          feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE      )

     recursively_load_weights(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )

     hf_model.save_pretrained(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=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(
           "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
       )
       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )

