code: 
  
  
  
  """simple docstring"""
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import torch
  import torch.nn as nn
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput
  from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
  from .modeling_utils import ModelMixin
  from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
  @dataclass
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					a__             =4_2
  
  
  
  class 							_UpperCAmelCase							(				a	,a   ):
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					@register_to_config
  					def __init__(      self ,			A = None ,			A = 3 ,			A = 3 ,			A = False ,			A = "positional" ,			A = 0 ,			A = True ,			A = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D") ,			A = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D") ,			A = (2_2_4, 4_4_8, 6_7_2, 8_9_6) ,			A = 2 ,			A = 1 ,			A = 1 ,			A = "conv" ,			A = "conv" ,			A = "silu" ,			A = 8 ,			A = 3_2 ,			A = 1E-5 ,			A = "default" ,			A = True ,			A = None ,			A = None ,			)							->      List[str]:
  									super().__init__()
  
  									_UpperCAmelCase							:						int       							=					sample_size
  									_UpperCAmelCase							:						Tuple       							=					block_out_channels[0] * 4
  
  									# Check inputs
  									if len(A	) != len(A	):
  													raise ValueError(
  													    f'Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'	)
  
  									if len(A	) != len(A	):
  													raise ValueError(
  													    f'Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'	)
  
  									# input
  									_UpperCAmelCase							:						int       							=					nn.Convad(A ,			block_out_channels[0] ,			kernel_size=3 ,			padding=(1, 1)	)
  
  									# time
  									if time_embedding_type == "fourier":
  													_UpperCAmelCase							:						str       							=					GaussianFourierProjection(embedding_size=block_out_channels[0] ,			scale=1_6	)
  													_UpperCAmelCase							:						Optional[int]       							=					2 * block_out_channels[0]
  									elif time_embedding_type == "positional":
  													_UpperCAmelCase							:						List[str]       							=					Timesteps(block_out_channels[0] ,			A ,			A	)
  													_UpperCAmelCase							:						List[Any]       							=					block_out_channels[0]
  
  									_UpperCAmelCase							:						List[str]       							=					TimestepEmbedding(A ,			A	)
  
  									# class embedding
  									if class_embed_type is None and num_class_embeds is not None:
  													_UpperCAmelCase							:						Optional[int]       							=					nn.Embedding(A ,			A	)
  									elif class_embed_type == "timestep":
  													_UpperCAmelCase							:						Union[str, Any]       							=					TimestepEmbedding(A ,			A	)
  									elif class_embed_type == "identity":
  													_UpperCAmelCase							:						int       							=					nn.Identity(A ,			A	)
  									else:
  													_UpperCAmelCase							:						Dict       							=					None
  
  									_UpperCAmelCase							:						Dict       							=					nn.ModuleList([]	)
  									_UpperCAmelCase							:						str       							=					None
  									_UpperCAmelCase							:						str       							=					nn.ModuleList([]	)
  
  									# down
  									_UpperCAmelCase							:						List[Any]       							=					block_out_channels[0]
  									for i, down_block_type in enumerate(A	):
  													_UpperCAmelCase							:						Any       							=					output_channel
  													_UpperCAmelCase							:						Dict       							=					block_out_channels[i]
  													_UpperCAmelCase							:						Any       							=					i == len(A	) - 1
  
  													_UpperCAmelCase							:						Union[str, Any]       							=					get_down_block(
  													    A ,			num_layers=A ,			in_channels=A ,			out_channels=A ,			temb_channels=A ,			add_downsample=not is_final_block ,			resnet_eps=A ,			resnet_act_fn=A ,			resnet_groups=A ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,			downsample_padding=A ,			resnet_time_scale_shift=A ,			downsample_type=A ,			)
  													self.down_blocks.append(A	)
  
  									# mid
  									_UpperCAmelCase							:						List[str]       							=					UNetMidBlockaD(
  									    in_channels=block_out_channels[-1] ,			temb_channels=A ,			resnet_eps=A ,			resnet_act_fn=A ,			output_scale_factor=A ,			resnet_time_scale_shift=A ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1] ,			resnet_groups=A ,			add_attention=A ,			)
  
  									# up
  									_UpperCAmelCase							:						Dict       							=					list(reversed(A	)	)
  									_UpperCAmelCase							:						List[Any]       							=					reversed_block_out_channels[0]
  									for i, up_block_type in enumerate(A	):
  													_UpperCAmelCase							:						Optional[int]       							=					output_channel
  													_UpperCAmelCase							:						Optional[int]       							=					reversed_block_out_channels[i]
  													_UpperCAmelCase							:						int       							=					reversed_block_out_channels[min(i + 1 ,			len(A	) - 1	)]
  
  													_UpperCAmelCase							:						str       							=					i == len(A	) - 1
  
  													_UpperCAmelCase							:						Dict       							=					get_up_block(
  													    A ,			num_layers=layers_per_block + 1 ,			in_channels=A ,			out_channels=A ,			prev_output_channel=A ,			temb_channels=A ,			add_upsample=not is_final_block ,			resnet_eps=A ,			resnet_act_fn=A ,			resnet_groups=A ,			attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel ,			resnet_time_scale_shift=A ,			upsample_type=A ,			)
  													self.up_blocks.append(A	)
  													_UpperCAmelCase							:						Union[str, Any]       							=					output_channel
  
  									# out
  									_UpperCAmelCase							:						Any       							=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,			3_2	)
  									_UpperCAmelCase							:						Union[str, Any]       							=					nn.GroupNorm(num_channels=block_out_channels[0] ,			num_groups=A ,			eps=A	)
  									_UpperCAmelCase							:						List[Any]       							=					nn.SiLU()
  									_UpperCAmelCase							:						Any       							=					nn.Convad(block_out_channels[0] ,			A ,			kernel_size=3 ,			padding=1	)
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self ,			A ,			A ,			A = None ,			A = True ,			)							->      Union[UNetaDOutput, Tuple]:
  									# 0. center input if necessary
  									if self.config.center_input_sample:
  													_UpperCAmelCase							:						List[str]       							=					2 * sample - 1.0
  
  									# 1. time
  									_UpperCAmelCase							:						Optional[int]       							=					timestep
  									if not torch.is_tensor(A	):
  													_UpperCAmelCase							:						Tuple       							=					torch.tensor([timesteps] ,			dtype=torch.long ,			device=sample.device	)
  									elif torch.is_tensor(A	) and len(timesteps.shape	) == 0:
  													_UpperCAmelCase							:						int       							=					timesteps[None].to(sample.device	)
  
  									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
  									_UpperCAmelCase							:						Optional[Any]       							=					timesteps * torch.ones(sample.shape[0] ,			dtype=timesteps.dtype ,			device=timesteps.device	)
  
  									_UpperCAmelCase							:						Union[str, Any]       							=					self.time_proj(A	)
  
  									# timesteps does not contain any weights and will always return f32 tensors
  									# but time_embedding might actually be running in fp16. so we need to cast here.
  									# there might be better ways to encapsulate this.
  									_UpperCAmelCase							:						List[Any]       							=					t_emb.to(dtype=self.dtype	)
  									_UpperCAmelCase							:						Optional[Any]       							=					self.time_embedding(A	)
  
  									if self.class_embedding is not None:
  													if class_labels is None:
  																	raise ValueError('''class_labels should be provided when doing class conditioning'''	)
  
  													if self.config.class_embed_type == "timestep":
  																	_UpperCAmelCase							:						List[str]       							=					self.time_proj(A	)
  
  													_UpperCAmelCase							:						Optional[Any]       							=					self.class_embedding(A	).to(dtype=self.dtype	)
  													_UpperCAmelCase							:						Optional[Any]       							=					emb + class_emb
  
  									# 2. pre-process
  									_UpperCAmelCase							:						Optional[int]       							=					sample
  									_UpperCAmelCase							:						Tuple       							=					self.conv_in(A	)
  
  									# 3. down
  									_UpperCAmelCase							:						str       							=					(sample,)
  									for downsample_block in self.down_blocks:
  													if hasattr(A ,			'''skip_conv'''	):
  																	_UpperCAmelCase							:						List[Any]       							=					downsample_block(
  																	    hidden_states=A ,			temb=A ,			skip_sample=A	)
  													else:
  																	_UpperCAmelCase							:						List[Any]       							=					downsample_block(hidden_states=A ,			temb=A	)
  
  													down_block_res_samples += res_samples
  
  									# 4. mid
  									_UpperCAmelCase							:						List[Any]       							=					self.mid_block(A ,			A	)
  
  									# 5. up
  									_UpperCAmelCase							:						Union[str, Any]       							=					None
  									for upsample_block in self.up_blocks:
  													_UpperCAmelCase							:						Dict       							=					down_block_res_samples[-len(upsample_block.resnets	) :]
  													_UpperCAmelCase							:						Any       							=					down_block_res_samples[: -len(upsample_block.resnets	)]
  
  													if hasattr(A ,			'''skip_conv'''	):
  																	_UpperCAmelCase							:						Any       							=					upsample_block(A ,			A ,			A ,			A	)
  													else:
  																	_UpperCAmelCase							:						List[Any]       							=					upsample_block(A ,			A ,			A	)
  
          # 6. post-process
  									_UpperCAmelCase							:						Dict       							=					self.conv_norm_out(A	)
  									_UpperCAmelCase							:						Any       							=					self.conv_act(A	)
  									_UpperCAmelCase							:						Optional[Any]       							=					self.conv_out(A	)
  
  									if skip_sample is not None:
  													sample += skip_sample
  
  									if self.config.time_embedding_type == "fourier":
  													_UpperCAmelCase							:						Union[str, Any]       							=					timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]	)))	)
  													_UpperCAmelCase							:						Union[str, Any]       							=					sample / timesteps
  
  									if not return_dict:
  													return (sample,)
  
  									return UNetaDOutput(sample=A	)
  
  
  
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
    )
  
  
  