code: 
  
  
  import math
  import warnings
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  from scipy import integrate
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
  class 						A__      (					UpperCamelCase_			):
      __magic_name__    						=	42
      __magic_name__    						=	None
  
  
  
  
  
  
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_=0.999							,UpperCamelCase_="cosine"							,):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if alpha_transform_type == "cosine":
  
            def alpha_bar_fn(UpperCamelCase_       ):
                 return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2
  
       elif alpha_transform_type == "exp":
  
            def alpha_bar_fn(UpperCamelCase_       ):
                 return math.exp(t * -12.0       )
  
       else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''       )
  
       snake_case					=       []
       for i in range(snake_case_       ):
            snake_case					=       i / num_diffusion_timesteps
            snake_case					=       (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(snake_case_       ) / alpha_bar_fn(snake_case_       )							,snake_case_       )       )
       return torch.tensor(snake_case_							,dtype=torch.floataa       )
  
  class 						A__      (					UpperCamelCase_ ,      UpperCamelCase_			):
      __magic_name__    						=	[e.name for e in KarrasDiffusionSchedulers]
      __magic_name__    						=	1
      @register_to_config
      def __init__( self    ,      __snake_case = 1_0_0_0    ,      __snake_case = 0.0001    ,      __snake_case = 0.02    ,      __snake_case = "linear"    ,      __snake_case = None    ,      __snake_case = False    ,      __snake_case = "epsilon"    ,      __snake_case = "linspace"    ,      __snake_case = 0    ,      ):
  
  
           if trained_betas is not None:
                snake_case					=       torch.tensor(__UpperCamelCase    ,      dtype=torch.floataa					)
           elif beta_schedule == "linear":
                snake_case					=       torch.linspace(__UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase    ,      dtype=torch.floataa					)
           elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                snake_case					=       (
                    torch.linspace(beta_start**0.5    ,      beta_end**0.5    ,      __UpperCamelCase    ,      dtype=torch.floataa					) ** 2
                )
           elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                snake_case					=       betas_for_alpha_bar(__UpperCamelCase					)
           else:
                raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)
  
           snake_case					=       1.0 - self.betas
           snake_case					=       torch.cumprod(self.alphas    ,      dim=0					)
  
           snake_case					=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
           snake_case					=       np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
           snake_case					=       torch.from_numpy(__UpperCamelCase					)
  
           # setable values
           snake_case					=       None
           snake_case					=       use_karras_sigmas
           self.set_timesteps(__UpperCamelCase    ,      __UpperCamelCase					)
           snake_case					=       []
           snake_case					=       False
      @property
      def 	a_    ( self					):
  
  
           if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()
  
           return (self.sigmas.max() ** 2 + 1) ** 0.5
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
  
  
           if isinstance(__UpperCamelCase    ,      torch.Tensor					):
                snake_case					=       timestep.to(self.timesteps.device					)
           snake_case					=       (self.timesteps == timestep).nonzero().item()
           snake_case					=       self.sigmas[step_index]
           snake_case					=       sample / ((sigma**2 + 1) ** 0.5)
           snake_case					=       True
           return sample
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
  
  
           def lms_derivative(__snake_case					):
                snake_case					=       1.0
                for k in range(__UpperCamelCase					):
                     if current_order == k:
                          continue
                     prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
                return prod
  
           snake_case					=       integrate.quad(__UpperCamelCase    ,      self.sigmas[t]    ,      self.sigmas[t + 1]    ,      epsrel=1E-4					)[0]
  
           return integrated_coeff
      def 	a_    ( self    ,      __snake_case    ,      __snake_case = None					):
           snake_case					=       num_inference_steps
  
           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
                snake_case					=       np.linspace(0    ,      self.config.num_train_timesteps - 1    ,      __UpperCamelCase    ,      dtype=__UpperCamelCase					)[
                    ::-1
                ].copy()
           elif self.config.timestep_spacing == "leading":
                snake_case					=       self.config.num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                snake_case					=       (np.arange(0    ,      __UpperCamelCase					) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase					)
                timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                snake_case					=       self.config.num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                snake_case					=       (np.arange(self.config.num_train_timesteps    ,      0    ,      -step_ratio					)).round().copy().astype(__UpperCamelCase					)
                timesteps -= 1
           else:
                raise ValueError(
                    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)
  
           snake_case					=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
           snake_case					=       np.log(__UpperCamelCase					)
           snake_case					=       np.interp(__UpperCamelCase    ,      np.arange(0    ,      len(__UpperCamelCase					)					)    ,      __UpperCamelCase					)
  
           if self.use_karras_sigmas:
                snake_case					=       self._convert_to_karras(in_sigmas=__UpperCamelCase					)
                snake_case					=       np.array([self._sigma_to_t(__UpperCamelCase    ,      __UpperCamelCase					) for sigma in sigmas]					)
  
           snake_case					=       np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
  
           snake_case					=       torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)
           if str(__UpperCamelCase					).startswith('''mps'''					):
                # mps does not support float64
                snake_case					=       torch.from_numpy(__UpperCamelCase					).to(__UpperCamelCase    ,      dtype=torch.floataa					)
           else:
                snake_case					=       torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)
  
           snake_case					=       []
      def 	a_    ( self    ,      __snake_case    ,      __snake_case					):
           snake_case					=       np.log(__UpperCamelCase					)
  
           # get distribution
           snake_case					=       log_sigma - log_sigmas[:, np.newaxis]
  
           # get sigmas range
           snake_case					=       np.cumsum((dists >= 0)    ,      axis=0					).argmax(axis=0					).clip(max=log_sigmas.shape[0] - 2					)
           snake_case					=       low_idx + 1
  
           snake_case					=       log_sigmas[low_idx]
           snake_case					=       log_sigmas[high_idx]
  
           # interpolate sigmas
           snake_case					=       (low - log_sigma) / (low - high)
           snake_case					=       np.clip(__UpperCamelCase    ,      0    ,      1					)
  
           # transform interpolation to time range
           snake_case					=       (1 - w) * low_idx + w * high_idx
           snake_case					=       t.reshape(sigma.shape					)
           return t
      def 	a_    ( self    ,      __snake_case					):
           snake_case					=       in_sigmas[-1].item()
           snake_case					=       in_sigmas[0].item()
  
           snake_case					=       7.0  # 7.0 is the value used in the paper
           snake_case					=       np.linspace(0    ,      1    ,      self.num_inference_steps					)
           snake_case					=       sigma_min ** (1 / rho)
           snake_case					=       sigma_max ** (1 / rho)
           snake_case					=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      __snake_case = 4    ,      __snake_case = True    ,      ):
  
  
           if not self.is_scale_input_called:
                warnings.warn(
                    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                    '''See `StableDiffusionPipeline` for a usage example.'''					)
  
           if isinstance(__UpperCamelCase    ,      torch.Tensor					):
                snake_case					=       timestep.to(self.timesteps.device					)
           snake_case					=       (self.timesteps == timestep).nonzero().item()
           snake_case					=       self.sigmas[step_index]
  
           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                snake_case					=       sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                snake_case					=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           elif self.config.prediction_type == "sample":
                snake_case					=       model_output
           else:
                raise ValueError(
                    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)
  
           # 2. Convert to an ODE derivative
           snake_case					=       (sample - pred_original_sample) / sigma
           self.derivatives.append(__UpperCamelCase					)
           if len(self.derivatives					) > order:
                self.derivatives.pop(0					)
  
           # 3. Compute linear multistep coefficients
           snake_case					=       min(step_index + 1    ,      __UpperCamelCase					)
           snake_case					=       [self.get_lms_coefficient(__UpperCamelCase    ,      __UpperCamelCase    ,      __UpperCamelCase					) for curr_order in range(__UpperCamelCase					)]
  
           # 4. Compute previous sample based on the derivatives path
           snake_case					=       sample + sum(
               coeff * derivative for coeff, derivative in zip(__UpperCamelCase    ,      reversed(self.derivatives					)					)					)
  
           if not return_dict:
                return (prev_sample,)
  
           return LMSDiscreteSchedulerOutput(prev_sample=__UpperCamelCase    ,      pred_original_sample=__UpperCamelCase					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case    ,      __snake_case    ,      ):
           snake_case					=       self.sigmas.to(device=original_samples.device    ,      dtype=original_samples.dtype					)
           if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase					):
                # mps does not support float64
                snake_case					=       self.timesteps.to(original_samples.device    ,      dtype=torch.floataa					)
                snake_case					=       timesteps.to(original_samples.device    ,      dtype=torch.floataa					)
           else:
                snake_case					=       self.timesteps.to(original_samples.device					)
                snake_case					=       timesteps.to(original_samples.device					)
  
           snake_case					=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
           snake_case					=       sigmas[step_indices].flatten()
           while len(sigma.shape					) < len(original_samples.shape					):
                snake_case					=       sigma.unsqueeze(-1					)
  
           snake_case					=       original_samples + noise * sigma
           return noisy_samples
  
  
  
  
      def __len__( self					):
           return self.config.num_train_timesteps
  
  
  
  
  
style_context: 
  
  
  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 (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  _SCREAMING_SNAKE_CASE    :				List[str]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					{
      "post_extract_proj": "feature_projection",
      "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
      "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
      "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
      "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
      "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
      "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
      "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
      "output.dense": "encoder.encoder.layer.*.output.dense",
      "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
      "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
      "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
      "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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       fairseq_model.state_dict()
  
       snake_case					=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
       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
            else:
                 for key, mapped_key in MAPPING.items():
                      snake_case					=       '''sew_d.''' + 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]:
                           snake_case					=       True
                           if "*" in mapped_key:
                                snake_case					=       name.split(UpperCamelCase_       )[0].split('''.'''       )[-2]
                                if not layer_index.isnumeric():
                                     continue
                                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 "weight" in name:
                                snake_case					=       '''weight'''
                           elif "bias" in name:
                                snake_case					=       '''bias'''
                           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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       SEWDConfig()
       if is_finetuned:
            snake_case					=       model.wav_encoder.wav_model.cfg
       else:
            snake_case					=       model.cfg
  
       snake_case					=       fs_config.conv_bias
       snake_case					=       eval(fs_config.conv_feature_layers       )
       snake_case					=       [x[0] for x in conv_layers]
       snake_case					=       [x[1] for x in conv_layers]
       snake_case					=       [x[2] for x in conv_layers]
       snake_case					=       '''gelu'''
       snake_case					=       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       snake_case					=       0.0
       snake_case					=       fs_config.activation_fn.name
       snake_case					=       fs_config.encoder_embed_dim
       snake_case					=       0.02
       snake_case					=       fs_config.encoder_ffn_embed_dim
       snake_case					=       1e-5
       snake_case					=       fs_config.encoder_layerdrop
       snake_case					=       fs_config.encoder_attention_heads
       snake_case					=       fs_config.conv_pos_groups
       snake_case					=       fs_config.conv_pos
       snake_case					=       len(UpperCamelCase_       )
       snake_case					=       fs_config.encoder_layers
       snake_case					=       fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       snake_case					=       fs_config.max_position_embeddings
       snake_case					=       fs_config.position_buckets
       snake_case					=       fs_config.share_att_key
       snake_case					=       fs_config.relative_attention
       snake_case					=       fs_config.position_biased_input
       snake_case					=       tuple(fs_config.pos_att_type.split('''|'''       )       )
       snake_case					=       fs_config.norm_rel_ebd
  
       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
            snake_case					=       model.cfg
            snake_case					=       fs_config.final_dropout
            snake_case					=       fs_config.layerdrop
       snake_case					=       fs_config.activation_dropout
       snake_case					=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       snake_case					=       fs_config.attention_dropout
       snake_case					=       fs_config.dropout_input
       snake_case					=       fs_config.dropout
       snake_case					=       fs_config.mask_channel_length
       snake_case					=       fs_config.mask_channel_prob
       snake_case					=       fs_config.mask_length
       snake_case					=       fs_config.mask_prob
  
       snake_case					=       '''Wav2Vec2FeatureExtractor'''
       snake_case					=       '''Wav2Vec2CTCTokenizer'''
  
       return config
  @torch.no_grad()
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if is_finetuned:
            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				,						snake_case				,						snake_case					=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )
  
       if config_path is not None:
            snake_case					=       SEWDConfig.from_pretrained(UpperCamelCase_       )
       else:
            snake_case					=       convert_config(model[0]							,UpperCamelCase_       )
       snake_case					=       model[0].eval()
  
       snake_case					=       True if config.feat_extract_norm == '''layer''' else False
       snake_case					=       WavaVecaFeatureExtractor(
           feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)
  
       if is_finetuned:
            if dict_path:
                 snake_case					=       Dictionary.load(UpperCamelCase_       )
  
                 # 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.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(UpperCamelCase_							,'''vocab.json'''       )
                 if not os.path.isdir(UpperCamelCase_       ):
                      logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_       )       )
                      return
                 os.makedirs(UpperCamelCase_							,exist_ok=UpperCamelCase_       )
                 with open(UpperCamelCase_							,'''w'''							,encoding='''utf-8'''       ) as vocab_handle:
                      json.dump(target_dict.indices							,UpperCamelCase_       )
                 snake_case					=       WavaVecaCTCTokenizer(
                     UpperCamelCase_							,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=UpperCamelCase_							,)
                 snake_case					=       WavaVecaProcessor(feature_extractor=UpperCamelCase_							,tokenizer=UpperCamelCase_       )
                 processor.save_pretrained(UpperCamelCase_       )
  
            snake_case					=       SEWDForCTC(UpperCamelCase_       )
       else:
            snake_case					=       SEWDModel(UpperCamelCase_       )
            feature_extractor.save_pretrained(UpperCamelCase_       )
  
       recursively_load_weights(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       hf_model.save_pretrained(UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				List[Any]       =					argparse.ArgumentParser()
   parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
   parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
   parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
   parser.add_argument("--config_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    :				Optional[int]       =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )
  
  
  
  
  