code: 
  
  
  
  """simple docstring"""
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
  		import torch
  
  if is_vision_available():
  		from PIL import Image
  
  		from transformers import ViltImageProcessor
  class 							_UpperCAmelCase							(				unittest.TestCase   ):
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(      self ,			A ,			A=7 ,			A=3 ,			A=1_8 ,			A=3_0 ,			A=4_0_0 ,			A=True ,			A=None ,			A=2 ,			A=True ,			A=[0.5, 0.5, 0.5] ,			A=[0.5, 0.5, 0.5] ,			)							->      Any:
  									_UpperCAmelCase							:						List[str]       							=					size if size is not None else {'''shortest_edge''': 3_0}
  									_UpperCAmelCase							:						Optional[int]       							=					parent
  									_UpperCAmelCase							:						List[str]       							=					batch_size
  									_UpperCAmelCase							:						int       							=					num_channels
  									_UpperCAmelCase							:						int       							=					image_size
  									_UpperCAmelCase							:						Any       							=					min_resolution
  									_UpperCAmelCase							:						int       							=					max_resolution
  									_UpperCAmelCase							:						str       							=					do_resize
  									_UpperCAmelCase							:						str       							=					size
  									_UpperCAmelCase							:						Optional[int]       							=					size_divisor
  									_UpperCAmelCase							:						Union[str, Any]       							=					do_normalize
  									_UpperCAmelCase							:						Any       							=					image_mean
  									_UpperCAmelCase							:						List[Any]       							=					image_std
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self	)							->      Optional[Any]:
  									return {
  									    "image_mean": self.image_mean,
  									    "image_std": self.image_std,
  									    "do_normalize": self.do_normalize,
  									    "do_resize": self.do_resize,
  									    "size": self.size,
  									    "size_divisor": self.size_divisor,
  									}
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self ,			A ,			A=False	)							->      List[str]:
  									if not batched:
  													_UpperCAmelCase							:						Any       							=					self.size['''shortest_edge''']
  													_UpperCAmelCase							:						int       							=					image_inputs[0]
  													if isinstance(A ,			Image.Image	):
  																	_UpperCAmelCase							:						List[str]       							=					image.size
  													else:
  																	_UpperCAmelCase							:						Optional[Any]       							=					image.shape[1], image.shape[2]
  													_UpperCAmelCase							:						str       							=					size / min(A ,			A	)
  													if h < w:
  																	_UpperCAmelCase							:						Optional[int]       							=					size, scale * w
  													else:
  																	_UpperCAmelCase							:						List[Any]       							=					scale * h, size
  
  													_UpperCAmelCase							:						Tuple       							=					int((1_3_3_3 / 8_0_0) * size	)
  													if max(A ,			A	) > max_size:
  																	_UpperCAmelCase							:						List[str]       							=					max_size / max(A ,			A	)
  																	_UpperCAmelCase							:						Dict       							=					newh * scale
  																	_UpperCAmelCase							:						Optional[int]       							=					neww * scale
  
  													_UpperCAmelCase							:						str       							=					int(newh + 0.5	), int(neww + 0.5	)
  													_UpperCAmelCase							:						List[str]       							=					(
  													    newh // self.size_divisor * self.size_divisor,
  													    neww // self.size_divisor * self.size_divisor,
  													)
  
  									else:
  													_UpperCAmelCase							:						List[Any]       							=					[]
  													for image in image_inputs:
  																	_UpperCAmelCase							:						Optional[int]       							=					self.get_expected_values([image]	)
  																	expected_values.append((expected_height, expected_width)	)
  													_UpperCAmelCase							:						Optional[Any]       							=					max(A ,			key=lambda A	: item[0]	)[0]
  													_UpperCAmelCase							:						Dict       							=					max(A ,			key=lambda A	: item[1]	)[1]
  
  									return expected_height, expected_width
  
  
  
  @require_torch
  @require_vision
  class 							_UpperCAmelCase							(				a	,unittest.TestCase   ):
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					a__             =ViltImageProcessor if is_vision_available() else None
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self	)							->      Dict:
  									_UpperCAmelCase							:						int       							=					ViltImageProcessingTester(self	)
  
  
  
  
  
  					@property
  					def 				__lowerCAmelCase			(      self	)							->      List[str]:
  									return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self	)							->      str:
  									_UpperCAmelCase							:						Optional[Any]       							=					self.image_processing_class(**self.image_processor_dict	)
  									self.assertTrue(hasattr(A ,			'''image_mean'''	)	)
  									self.assertTrue(hasattr(A ,			'''image_std'''	)	)
  									self.assertTrue(hasattr(A ,			'''do_normalize'''	)	)
  									self.assertTrue(hasattr(A ,			'''do_resize'''	)	)
  									self.assertTrue(hasattr(A ,			'''size'''	)	)
  									self.assertTrue(hasattr(A ,			'''size_divisor'''	)	)
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self	)							->      int:
  									_UpperCAmelCase							:						List[str]       							=					self.image_processing_class.from_dict(self.image_processor_dict	)
  									self.assertEqual(image_processor.size ,			{'''shortest_edge''': 3_0}	)
  
  									_UpperCAmelCase							:						List[str]       							=					self.image_processing_class.from_dict(self.image_processor_dict ,			size=4_2	)
  									self.assertEqual(image_processor.size ,			{'''shortest_edge''': 4_2}	)
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self	)							->      int:
  									pass
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self	)							->      Dict:
  									# Initialize image_processing
  									_UpperCAmelCase							:						str       							=					self.image_processing_class(**self.image_processor_dict	)
  									# create random PIL images
  									_UpperCAmelCase							:						Optional[int]       							=					prepare_image_inputs(self.image_processor_tester ,			equal_resolution=A	)
  									for image in image_inputs:
  													self.assertIsInstance(A ,			Image.Image	)
  
  									# Test not batched input
  									_UpperCAmelCase							:						int       							=					image_processing(image_inputs[0] ,			return_tensors='''pt'''	).pixel_values
  
  									_UpperCAmelCase							:						Union[str, Any]       							=					self.image_processor_tester.get_expected_values(A	)
  									self.assertEqual(
  									    encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)
  
  									# Test batched
  									_UpperCAmelCase							:						int       							=					image_processing(A ,			return_tensors='''pt'''	).pixel_values
  
  									_UpperCAmelCase							:						Optional[int]       							=					self.image_processor_tester.get_expected_values(A ,			batched=A	)
  									self.assertEqual(
  									    encoded_images.shape ,			(
  									        self.image_processor_tester.batch_size,
  									        self.image_processor_tester.num_channels,
  									        expected_height,
  									        expected_width,
  									    ) ,			)
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self	)							->      int:
  									# Initialize image_processing
  									_UpperCAmelCase							:						List[Any]       							=					self.image_processing_class(**self.image_processor_dict	)
  									# create random numpy tensors
  									_UpperCAmelCase							:						List[Any]       							=					prepare_image_inputs(self.image_processor_tester ,			equal_resolution=A ,			numpify=A	)
  									for image in image_inputs:
  													self.assertIsInstance(A ,			np.ndarray	)
  
  									# Test not batched input
  									_UpperCAmelCase							:						List[Any]       							=					image_processing(image_inputs[0] ,			return_tensors='''pt'''	).pixel_values
  
  									_UpperCAmelCase							:						int       							=					self.image_processor_tester.get_expected_values(A	)
  									self.assertEqual(
  									    encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)
  
  									# Test batched
  									_UpperCAmelCase							:						List[Any]       							=					image_processing(A ,			return_tensors='''pt'''	).pixel_values
  
  									_UpperCAmelCase							:						Optional[int]       							=					self.image_processor_tester.get_expected_values(A ,			batched=A	)
  									self.assertEqual(
  									    encoded_images.shape ,			(
  									        self.image_processor_tester.batch_size,
  									        self.image_processor_tester.num_channels,
  									        expected_height,
  									        expected_width,
  									    ) ,			)
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self	)							->      Dict:
  									# Initialize image_processing
  									_UpperCAmelCase							:						List[str]       							=					self.image_processing_class(**self.image_processor_dict	)
  									# create random PyTorch tensors
  									_UpperCAmelCase							:						Dict       							=					prepare_image_inputs(self.image_processor_tester ,			equal_resolution=A ,			torchify=A	)
  									for image in image_inputs:
  													self.assertIsInstance(A ,			torch.Tensor	)
  
  									# Test not batched input
  									_UpperCAmelCase							:						Any       							=					image_processing(image_inputs[0] ,			return_tensors='''pt'''	).pixel_values
  
  									_UpperCAmelCase							:						List[Any]       							=					self.image_processor_tester.get_expected_values(A	)
  									self.assertEqual(
  									    encoded_images.shape ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,			)
  
  									# Test batched
  									_UpperCAmelCase							:						Optional[Any]       							=					image_processing(A ,			return_tensors='''pt'''	).pixel_values
  
  									_UpperCAmelCase							:						Tuple       							=					self.image_processor_tester.get_expected_values(A ,			batched=A	)
  									self.assertEqual(
  									    encoded_images.shape ,			(
  									        self.image_processor_tester.batch_size,
  									        self.image_processor_tester.num_channels,
  									        expected_height,
  									        expected_width,
  									    ) ,			)
  
  
  
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
    )
  
  
  