code: 
  
  
  
  """simple docstring"""
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  _lowerCAmelCase					:Optional[int]  =      logging.get_logger(__name__)  # pylint: disable=invalid-name
  @dataclass
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =42
  
  
  
  class 							_UpperCAmelCase							(				a	,a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =1
  
  
  
  
  
       @register_to_config
       def __init__(      self ,			A = 4_0 ,			A = 0.002 ,			A = 80.0 ,			A = 0.5 ,			A = 1.0 ,			A = 7.0 ,			A = True ,			)							->      Dict:
           # standard deviation of the initial noise distribution
           _UpperCAmelCase							:						str       							=					sigma_max
  
           _UpperCAmelCase							:						Any       							=					np.linspace(0 ,			1 ,			A	)
           _UpperCAmelCase							:						Dict       							=					self._convert_to_karras(A	)
           _UpperCAmelCase							:						Dict       							=					self.sigma_to_t(A	)
  
           # setable values
           _UpperCAmelCase							:						Dict       							=					None
           _UpperCAmelCase							:						Optional[Any]       							=					torch.from_numpy(A	)
           _UpperCAmelCase							:						Any       							=					torch.from_numpy(A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					False
           _UpperCAmelCase							:						int       							=					False
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None	)							->      Optional[int]:
           if schedule_timesteps is None:
               _UpperCAmelCase							:						Optional[Any]       							=					self.timesteps
  
           _UpperCAmelCase							:						Optional[int]       							=					(schedule_timesteps == timestep).nonzero()
           return indices.item()
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      torch.FloatTensor:
           # Get sigma corresponding to timestep
           if isinstance(A ,			torch.Tensor	):
               _UpperCAmelCase							:						Any       							=					timestep.to(self.timesteps.device	)
           _UpperCAmelCase							:						str       							=					self.index_for_timestep(A	)
           _UpperCAmelCase							:						List[str]       							=					self.sigmas[step_idx]
  
           _UpperCAmelCase							:						Optional[Any]       							=					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
           _UpperCAmelCase							:						Dict       							=					True
           return sample
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Tuple:
           if not isinstance(A ,			np.ndarray	):
               _UpperCAmelCase							:						str       							=					np.array(A ,			dtype=np.floataa	)
  
           _UpperCAmelCase							:						Tuple       							=					1_0_0_0 * 0.25 * np.log(sigmas + 1E-44	)
  
           return timesteps
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A = None ,			A = None ,			A = None ,			)							->      int:
           if num_inference_steps is None and timesteps is None:
               raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''	)
  
           if num_inference_steps is not None and timesteps is not None:
               raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''	)
  
           # Follow DDPMScheduler custom timesteps logic
           if timesteps is not None:
               for i in range(1 ,			len(A	)	):
                   if timesteps[i] >= timesteps[i - 1]:
                       raise ValueError('''`timesteps` must be in descending order.'''	)
  
               if timesteps[0] >= self.config.num_train_timesteps:
                   raise ValueError(
                       f'`timesteps` must start before `self.config.train_timesteps`:'
                       f' {self.config.num_train_timesteps}.'	)
  
               _UpperCAmelCase							:						Optional[int]       							=					np.array(A ,			dtype=np.intaa	)
               _UpperCAmelCase							:						Optional[int]       							=					True
           else:
               if num_inference_steps > self.config.num_train_timesteps:
                   raise ValueError(
                       f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                       f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                       f' maximal {self.config.num_train_timesteps} timesteps.'	)
  
               _UpperCAmelCase							:						Tuple       							=					num_inference_steps
  
               _UpperCAmelCase							:						Optional[Any]       							=					self.config.num_train_timesteps // self.num_inference_steps
               _UpperCAmelCase							:						Optional[Any]       							=					(np.arange(0 ,			A	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
               _UpperCAmelCase							:						Dict       							=					False
  
           # Map timesteps to Karras sigmas directly for multistep sampling
           # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
           _UpperCAmelCase							:						Optional[int]       							=					self.config.num_train_timesteps
           _UpperCAmelCase							:						Optional[int]       							=					timesteps[::-1].copy()
           _UpperCAmelCase							:						str       							=					ramp / (num_train_timesteps - 1)
           _UpperCAmelCase							:						Union[str, Any]       							=					self._convert_to_karras(A	)
           _UpperCAmelCase							:						List[str]       							=					self.sigma_to_t(A	)
  
           _UpperCAmelCase							:						int       							=					np.concatenate([sigmas, [self.sigma_min]]	).astype(np.floataa	)
           _UpperCAmelCase							:						Union[str, Any]       							=					torch.from_numpy(A	).to(device=A	)
  
           if str(A	).startswith('''mps'''	):
               # mps does not support float64
               _UpperCAmelCase							:						str       							=					torch.from_numpy(A	).to(A ,			dtype=torch.floataa	)
           else:
               _UpperCAmelCase							:						Dict       							=					torch.from_numpy(A	).to(device=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[Any]:
           _UpperCAmelCase							:						float       							=					self.config.sigma_min
           _UpperCAmelCase							:						float       							=					self.config.sigma_max
  
           _UpperCAmelCase							:						int       							=					self.config.rho
           _UpperCAmelCase							:						List[Any]       							=					sigma_min ** (1 / rho)
           _UpperCAmelCase							:						List[str]       							=					sigma_max ** (1 / rho)
           _UpperCAmelCase							:						List[Any]       							=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Any:
           _UpperCAmelCase							:						Tuple       							=					self.config.sigma_data
  
           _UpperCAmelCase							:						List[Any]       							=					sigma_data**2 / (sigma**2 + sigma_data**2)
           _UpperCAmelCase							:						Optional[int]       							=					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      List[Any]:
           _UpperCAmelCase							:						Tuple       							=					self.config.sigma_min
           _UpperCAmelCase							:						List[Any]       							=					self.config.sigma_data
  
           _UpperCAmelCase							:						Union[str, Any]       							=					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
           _UpperCAmelCase							:						str       							=					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
           return c_skip, c_out
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			A = None ,			A = True ,			)							->      Union[CMStochasticIterativeSchedulerOutput, Tuple]:
  
           if (
               isinstance(A ,			A	)
               or isinstance(A ,			torch.IntTensor	)
               or isinstance(A ,			torch.LongTensor	)
           ):
               raise ValueError(
                   (
                       '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                       f' `{self.__class__}.step()` is not supported. Make sure to pass'
                       ''' one of the `scheduler.timesteps` as a timestep.'''
                   ) ,			)
  
           if not self.is_scale_input_called:
               logger.warning(
                   '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                   '''See `StableDiffusionPipeline` for a usage example.'''	)
  
           if isinstance(A ,			torch.Tensor	):
               _UpperCAmelCase							:						Tuple       							=					timestep.to(self.timesteps.device	)
  
           _UpperCAmelCase							:						Dict       							=					self.config.sigma_min
           _UpperCAmelCase							:						Union[str, Any]       							=					self.config.sigma_max
  
           _UpperCAmelCase							:						str       							=					self.index_for_timestep(A	)
  
           # sigma_next corresponds to next_t in original implementation
           _UpperCAmelCase							:						List[Any]       							=					self.sigmas[step_index]
           if step_index + 1 < self.config.num_train_timesteps:
               _UpperCAmelCase							:						Tuple       							=					self.sigmas[step_index + 1]
           else:
               # Set sigma_next to sigma_min
               _UpperCAmelCase							:						Dict       							=					self.sigmas[-1]
  
           # Get scalings for boundary conditions
           _UpperCAmelCase							,       _UpperCAmelCase							:						List[Any]       							=					self.get_scalings_for_boundary_condition(A	)
  
           # 1. Denoise model output using boundary conditions
           _UpperCAmelCase							:						Optional[int]       							=					c_out * model_output + c_skip * sample
           if self.config.clip_denoised:
               _UpperCAmelCase							:						Optional[int]       							=					denoised.clamp(-1 ,			1	)
  
           # 2. Sample z ~ N(0, s_noise^2 * I)
           # Noise is not used for onestep sampling.
           if len(self.timesteps	) > 1:
               _UpperCAmelCase							:						Union[str, Any]       							=					randn_tensor(
                   model_output.shape ,			dtype=model_output.dtype ,			device=model_output.device ,			generator=A	)
           else:
               _UpperCAmelCase							:						Dict       							=					torch.zeros_like(A	)
           _UpperCAmelCase							:						Union[str, Any]       							=					noise * self.config.s_noise
  
           _UpperCAmelCase							:						Dict       							=					sigma_next.clamp(min=A ,			max=A	)
  
           # 3. Return noisy sample
           # tau = sigma_hat, eps = sigma_min
           _UpperCAmelCase							:						Union[str, Any]       							=					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
           if not return_dict:
               return (prev_sample,)
  
           return CMStochasticIterativeSchedulerOutput(prev_sample=A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A ,			)							->      torch.FloatTensor:
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           _UpperCAmelCase							:						str       							=					self.sigmas.to(device=original_samples.device ,			dtype=original_samples.dtype	)
           if original_samples.device.type == "mps" and torch.is_floating_point(A	):
               # mps does not support float64
               _UpperCAmelCase							:						Any       							=					self.timesteps.to(original_samples.device ,			dtype=torch.floataa	)
               _UpperCAmelCase							:						Dict       							=					timesteps.to(original_samples.device ,			dtype=torch.floataa	)
           else:
               _UpperCAmelCase							:						Any       							=					self.timesteps.to(original_samples.device	)
               _UpperCAmelCase							:						Optional[int]       							=					timesteps.to(original_samples.device	)
  
           _UpperCAmelCase							:						Optional[Any]       							=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
           _UpperCAmelCase							:						str       							=					sigmas[step_indices].flatten()
           while len(sigma.shape	) < len(original_samples.shape	):
               _UpperCAmelCase							:						int       							=					sigma.unsqueeze(-1	)
  
           _UpperCAmelCase							:						Optional[int]       							=					original_samples + noise * sigma
           return noisy_samples
  
  
  
  
  
       def __len__(      self	)							->      Optional[Any]:
           return self.config.num_train_timesteps
  
  
  
style_context: 
  
  
  
  """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 (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  _lowerCAmelCase					:int  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					: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 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	int							):
      for attribute in key.split('''.'''							):
          _UpperCAmelCase							:						Optional[Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							)
  
      if weight_type is not None:
          _UpperCAmelCase							:						Union[str, Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							).shape
      else:
          _UpperCAmelCase							:						Union[str, Any]       							=					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":
          _UpperCAmelCase							:						Dict       							=					value
      elif weight_type == "weight_g":
          _UpperCAmelCase							:						Optional[int]       							=					value
      elif weight_type == "weight_v":
          _UpperCAmelCase							:						List[Any]       							=					value
      elif weight_type == "bias":
          _UpperCAmelCase							:						Union[str, Any]       							=					value
      else:
          _UpperCAmelCase							:						Optional[int]       							=					value
  
      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	int							):
      _UpperCAmelCase							:						Dict       							=					[]
      _UpperCAmelCase							:						Optional[int]       							=					fairseq_model.state_dict()
  
      _UpperCAmelCase							:						Any       							=					hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
      for name, value in fairseq_dict.items():
          _UpperCAmelCase							:						str       							=					False
          if "conv_layers" in name:
              load_conv_layer(
                  UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , hf_model.config.feat_extract_norm == '''group'''  , )
              _UpperCAmelCase							:						Any       							=					True
          else:
              for key, mapped_key in MAPPING.items():
                  _UpperCAmelCase							:						str       							=					'''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]:
                      _UpperCAmelCase							:						Dict       							=					True
                      if "*" in mapped_key:
                          _UpperCAmelCase							:						List[str]       							=					name.split(UpperCamelCase__							)[0].split('''.'''							)[-2]
                          if not layer_index.isnumeric():
                              continue
                          _UpperCAmelCase							:						str       							=					mapped_key.replace('''*'''  , UpperCamelCase__							)
                      if "weight_g" in name:
                          _UpperCAmelCase							:						int       							=					'''weight_g'''
                      elif "weight_v" in name:
                          _UpperCAmelCase							:						Optional[int]       							=					'''weight_v'''
                      elif "weight" in name:
                          _UpperCAmelCase							:						int       							=					'''weight'''
                      elif "bias" in name:
                          _UpperCAmelCase							:						int       							=					'''bias'''
                      else:
                          _UpperCAmelCase							:						Dict       							=					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 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	str							):
      _UpperCAmelCase							:						List[Any]       							=					full_name.split('''conv_layers.'''							)[-1]
      _UpperCAmelCase							:						Any       							=					name.split('''.'''							)
      _UpperCAmelCase							:						Union[str, Any]       							=					int(items[0]							)
      _UpperCAmelCase							:						int       							=					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.'
              )
              _UpperCAmelCase							:						Tuple       							=					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.'
              )
              _UpperCAmelCase							:						Union[str, 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."
              )
              _UpperCAmelCase							:						Union[str, 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.'
              )
              _UpperCAmelCase							:						int       							=					value
              logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
      else:
          unused_weights.append(UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Any							):
      _UpperCAmelCase							:						Any       							=					SEWDConfig()
      if is_finetuned:
          _UpperCAmelCase							:						str       							=					model.wav_encoder.wav_model.cfg
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					model.cfg
  
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_bias
      _UpperCAmelCase							:						Optional[int]       							=					eval(fs_config.conv_feature_layers							)
      _UpperCAmelCase							:						Dict       							=					[x[0] for x in conv_layers]
      _UpperCAmelCase							:						int       							=					[x[1] for x in conv_layers]
      _UpperCAmelCase							:						Optional[int]       							=					[x[2] for x in conv_layers]
      _UpperCAmelCase							:						int       							=					'''gelu'''
      _UpperCAmelCase							:						int       							=					'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      _UpperCAmelCase							:						Tuple       							=					0.0
      _UpperCAmelCase							:						List[Any]       							=					fs_config.activation_fn.name
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.encoder_embed_dim
      _UpperCAmelCase							:						Optional[int]       							=					0.02
      _UpperCAmelCase							:						List[Any]       							=					fs_config.encoder_ffn_embed_dim
      _UpperCAmelCase							:						Dict       							=					1E-5
      _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.encoder_layerdrop
      _UpperCAmelCase							:						Tuple       							=					fs_config.encoder_attention_heads
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_pos_groups
      _UpperCAmelCase							:						Any       							=					fs_config.conv_pos
      _UpperCAmelCase							:						Optional[Any]       							=					len(UpperCamelCase__							)
      _UpperCAmelCase							:						Any       							=					fs_config.encoder_layers
      _UpperCAmelCase							:						Dict       							=					fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      _UpperCAmelCase							:						int       							=					fs_config.max_position_embeddings
      _UpperCAmelCase							:						Any       							=					fs_config.position_buckets
      _UpperCAmelCase							:						Optional[int]       							=					fs_config.share_att_key
      _UpperCAmelCase							:						Dict       							=					fs_config.relative_attention
      _UpperCAmelCase							:						List[Any]       							=					fs_config.position_biased_input
      _UpperCAmelCase							:						str       							=					tuple(fs_config.pos_att_type.split('''|'''							)							)
      _UpperCAmelCase							:						List[Any]       							=					fs_config.norm_rel_ebd
  
      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
          _UpperCAmelCase							:						Any       							=					model.cfg
          _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.final_dropout
          _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.layerdrop
      _UpperCAmelCase							:						str       							=					fs_config.activation_dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      _UpperCAmelCase							:						List[Any]       							=					fs_config.attention_dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.dropout_input
      _UpperCAmelCase							:						str       							=					fs_config.dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_channel_length
      _UpperCAmelCase							:						Tuple       							=					fs_config.mask_channel_prob
      _UpperCAmelCase							:						List[str]       							=					fs_config.mask_length
      _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.mask_prob
  
      _UpperCAmelCase							:						int       							=					'''Wav2Vec2FeatureExtractor'''
      _UpperCAmelCase							:						str       							=					'''Wav2Vec2CTCTokenizer'''
  
      return config
  
  
  
  
  
  @torch.no_grad()
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	int  , UpperCamelCase__	:	Tuple=None  , UpperCamelCase__	:	Any=None  , UpperCamelCase__	:	Union[str, Any]=True							):
  
      if is_finetuned:
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						int       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]  , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
      else:
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)
  
      if config_path is not None:
          _UpperCAmelCase							:						List[str]       							=					SEWDConfig.from_pretrained(UpperCamelCase__							)
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					convert_config(model[0]  , UpperCamelCase__							)
      _UpperCAmelCase							:						Dict       							=					model[0].eval()
  
      _UpperCAmelCase							:						List[str]       							=					True if config.feat_extract_norm == '''layer''' else False
      _UpperCAmelCase							:						Optional[int]       							=					WavaVecaFeatureExtractor(
          feature_size=1  , sampling_rate=1_6000  , padding_value=0  , do_normalize=UpperCamelCase__  , return_attention_mask=UpperCamelCase__  , )
  
      if is_finetuned:
          if dict_path:
              _UpperCAmelCase							:						List[str]       							=					Dictionary.load(UpperCamelCase__							)
  
              # important change bos & pad token id since CTC symbol is <pad> and
              # not <s> as in fairseq
              _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.pad_index
              _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.bos_index
              _UpperCAmelCase							:						int       							=					target_dict.pad_index
              _UpperCAmelCase							:						str       							=					target_dict.bos_index
              _UpperCAmelCase							:						Tuple       							=					target_dict.eos_index
              _UpperCAmelCase							:						List[str]       							=					len(target_dict.symbols							)
              _UpperCAmelCase							:						Optional[Any]       							=					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__							)
              _UpperCAmelCase							:						Any       							=					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__  , )
              _UpperCAmelCase							:						Dict       							=					WavaVecaProcessor(feature_extractor=UpperCamelCase__  , tokenizer=UpperCamelCase__							)
              processor.save_pretrained(UpperCamelCase__							)
  
          _UpperCAmelCase							:						Tuple       							=					SEWDForCTC(UpperCamelCase__							)
      else:
          _UpperCAmelCase							:						Any       							=					SEWDModel(UpperCamelCase__							)
          feature_extractor.save_pretrained(UpperCamelCase__							)
  
      recursively_load_weights(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
      hf_model.save_pretrained(UpperCamelCase__							)
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					:Union[str, 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'
    )
    _lowerCAmelCase					:int  =      parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )
  
  
  