code: 
  
  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()
  snake_case_    						=				logging.get_logger(__name__)
  
  snake_case_    						=				{
      '''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 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					Optional[int]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       for attribute in key.split('.'    ):
            lowercase__					:     List[Any]   = getattr(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       if weight_type is not None:
            lowercase__					:     List[str]   = getattr(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    ).shape
       else:
            lowercase__					:     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":
            lowercase__					:     str   = value
       elif weight_type == "weight_g":
            lowercase__					:     Optional[int]   = value
       elif weight_type == "weight_v":
            lowercase__					:     Optional[int]   = value
       elif weight_type == "bias":
            lowercase__					:     Optional[int]   = value
       else:
            lowercase__					:     str   = value
  
       logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					List[str]    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     int   = []
       lowercase__					:     Dict   = fairseq_model.state_dict()
  
       lowercase__					:     int   = hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
       for name, value in fairseq_dict.items():
            lowercase__					:     int   = False
            if "conv_layers" in name:
                 load_conv_layer(
                     SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  hf_model.config.feat_extract_norm == 'group'			,  )
                 lowercase__					:     Any   = True
            else:
                 for key, mapped_key in MAPPING.items():
                      lowercase__					:     List[Any]   = '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]:
                           lowercase__					:     Optional[int]   = True
                           if "*" in mapped_key:
                                lowercase__					:     Tuple   = name.split(SCREAMING_SNAKE_CASE_    )[0].split('.'    )[-2]
                                if not layer_index.isnumeric():
                                     continue
                                lowercase__					:     Tuple   = mapped_key.replace('*'			,  SCREAMING_SNAKE_CASE_    )
                           if "weight_g" in name:
                                lowercase__					:     str   = 'weight_g'
                           elif "weight_v" in name:
                                lowercase__					:     List[str]   = 'weight_v'
                           elif "weight" in name:
                                lowercase__					:     List[str]   = 'weight'
                           elif "bias" in name:
                                lowercase__					:     str   = 'bias'
                           else:
                                lowercase__					:     str   = None
                           set_recursively(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
                      continue
            if not is_used:
                 unused_weights.append(SCREAMING_SNAKE_CASE_    )
  
       logger.warning(f"""Unused weights: {unused_weights}"""    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Union[str, Any]   = full_name.split('conv_layers.'    )[-1]
       lowercase__					:     Tuple   = name.split('.'    )
       lowercase__					:     Optional[Any]   = int(items[0]    )
       lowercase__					:     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."""
                 )
                 lowercase__					:     List[str]   = value
                 logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
            elif "weight" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                     f"""{full_name} has size {value.shape}, but"""
                     f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                 )
                 lowercase__					:     Optional[int]   = value
                 logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                     f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                     " found."
                 )
                 lowercase__					:     Dict   = value
                 logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
            elif "weight" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                     f"""{full_name} has size {value.shape}, but"""
                     f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                 )
                 lowercase__					:     Union[str, Any]   = value
                 logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
       else:
            unused_weights.append(SCREAMING_SNAKE_CASE_    )
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					Dict    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[int]   = SEWDConfig()
       if is_finetuned:
            lowercase__					:     List[str]   = model.wav_encoder.wav_model.cfg
       else:
            lowercase__					:     Tuple   = model.cfg
  
       lowercase__					:     List[str]   = fs_config.conv_bias
       lowercase__					:     Any   = eval(fs_config.conv_feature_layers    )
       lowercase__					:     Optional[Any]   = [x[0] for x in conv_layers]
       lowercase__					:     Union[str, Any]   = [x[1] for x in conv_layers]
       lowercase__					:     Tuple   = [x[2] for x in conv_layers]
       lowercase__					:     Union[str, Any]   = 'gelu'
       lowercase__					:     Dict   = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       lowercase__					:     List[Any]   = 0.0
       lowercase__					:     Optional[Any]   = fs_config.activation_fn.name
       lowercase__					:     Optional[int]   = fs_config.encoder_embed_dim
       lowercase__					:     List[str]   = 0.02
       lowercase__					:     Union[str, Any]   = fs_config.encoder_ffn_embed_dim
       lowercase__					:     List[str]   = 1E-5
       lowercase__					:     str   = fs_config.encoder_layerdrop
       lowercase__					:     Tuple   = fs_config.encoder_attention_heads
       lowercase__					:     Optional[Any]   = fs_config.conv_pos_groups
       lowercase__					:     Optional[int]   = fs_config.conv_pos
       lowercase__					:     Union[str, Any]   = len(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     List[Any]   = fs_config.encoder_layers
       lowercase__					:     Any   = fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       lowercase__					:     Any   = fs_config.max_position_embeddings
       lowercase__					:     Optional[int]   = fs_config.position_buckets
       lowercase__					:     List[Any]   = fs_config.share_att_key
       lowercase__					:     List[Any]   = fs_config.relative_attention
       lowercase__					:     Optional[int]   = fs_config.position_biased_input
       lowercase__					:     Tuple   = tuple(fs_config.pos_att_type.split('|'    )    )
       lowercase__					:     Tuple   = fs_config.norm_rel_ebd
  
       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
            lowercase__					:     Any   = model.cfg
            lowercase__					:     Union[str, Any]   = fs_config.final_dropout
            lowercase__					:     str   = fs_config.layerdrop
       lowercase__					:     Optional[int]   = fs_config.activation_dropout
       lowercase__					:     List[Any]   = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       lowercase__					:     Optional[int]   = fs_config.attention_dropout
       lowercase__					:     Tuple   = fs_config.dropout_input
       lowercase__					:     List[Any]   = fs_config.dropout
       lowercase__					:     Optional[int]   = fs_config.mask_channel_length
       lowercase__					:     Optional[int]   = fs_config.mask_channel_prob
       lowercase__					:     Any   = fs_config.mask_length
       lowercase__					:     Optional[Any]   = fs_config.mask_prob
  
       lowercase__					:     List[Any]   = 'Wav2Vec2FeatureExtractor'
       lowercase__					:     Tuple   = 'Wav2Vec2CTCTokenizer'
  
       return config
  
  @torch.no_grad()
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					str=None			,  SCREAMING_SNAKE_CASE_	:					Optional[int]=None			,  SCREAMING_SNAKE_CASE_	:					Optional[int]=True    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if is_finetuned:
            lowercase__   ,				lowercase__   ,				lowercase__					:     Optional[int]   = fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]			,  arg_overrides={'data': '/'.join(dict_path.split('/'    )[:-1]    )}    )
       else:
            lowercase__   ,				lowercase__   ,				lowercase__					:     List[str]   = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )
  
       if config_path is not None:
            lowercase__					:     List[str]   = SEWDConfig.from_pretrained(SCREAMING_SNAKE_CASE_    )
       else:
            lowercase__					:     Dict   = convert_config(model[0]			,  SCREAMING_SNAKE_CASE_    )
       lowercase__					:     List[Any]   = model[0].eval()
  
       lowercase__					:     Optional[int]   = True if config.feat_extract_norm == 'layer' else False
       lowercase__					:     Union[str, Any]   = WavaVecaFeatureExtractor(
           feature_size=1			,  sampling_rate=16_000			,  padding_value=0			,  do_normalize=SCREAMING_SNAKE_CASE_			,  return_attention_mask=SCREAMING_SNAKE_CASE_			,  )
  
       if is_finetuned:
            if dict_path:
                 lowercase__					:     int   = Dictionary.load(SCREAMING_SNAKE_CASE_    )
  
                 # important change bos & pad token id since CTC symbol is <pad> and
                 # not <s> as in fairseq
                 lowercase__					:     Optional[int]   = target_dict.pad_index
                 lowercase__					:     Any   = target_dict.bos_index
                 lowercase__					:     Tuple   = target_dict.pad_index
                 lowercase__					:     Any   = target_dict.bos_index
                 lowercase__					:     Any   = target_dict.eos_index
                 lowercase__					:     int   = len(target_dict.symbols    )
                 lowercase__					:     Any   = os.path.join(SCREAMING_SNAKE_CASE_			,  'vocab.json'    )
                 if not os.path.isdir(SCREAMING_SNAKE_CASE_    ):
                      logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE_    )    )
                      return
                 os.makedirs(SCREAMING_SNAKE_CASE_			,  exist_ok=SCREAMING_SNAKE_CASE_    )
                 with open(SCREAMING_SNAKE_CASE_			,  'w'			,  encoding='utf-8'    ) as vocab_handle:
                      json.dump(target_dict.indices			,  SCREAMING_SNAKE_CASE_    )
                 lowercase__					:     Dict   = WavaVecaCTCTokenizer(
                     SCREAMING_SNAKE_CASE_			,  unk_token=target_dict.unk_word			,  pad_token=target_dict.pad_word			,  bos_token=target_dict.bos_word			,  eos_token=target_dict.eos_word			,  word_delimiter_token='|'			,  do_lower_case=SCREAMING_SNAKE_CASE_			,  )
                 lowercase__					:     Dict   = WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_			,  tokenizer=SCREAMING_SNAKE_CASE_    )
                 processor.save_pretrained(SCREAMING_SNAKE_CASE_    )
  
            lowercase__					:     Tuple   = SEWDForCTC(SCREAMING_SNAKE_CASE_    )
       else:
            lowercase__					:     List[Any]   = SEWDModel(SCREAMING_SNAKE_CASE_    )
            feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_    )
  
       recursively_load_weights(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
  
       hf_model.save_pretrained(SCREAMING_SNAKE_CASE_    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				argparse.ArgumentParser()
         parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
         parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
         parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
         parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
         parser.add_argument(
             '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
         )
         snake_case_    						=				parser.parse_args()
         convert_sew_checkpoint(
             args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
         )
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  snake_case_    						=				{
      '''configuration_donut_swin''': ['''DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DonutSwinConfig'''],
      '''processing_donut''': ['''DonutProcessor'''],
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DonutSwinModel''',
             '''DonutSwinPreTrainedModel''',
         ]
  
  try:
         if not is_vision_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				['''DonutFeatureExtractor''']
         snake_case_    						=				['''DonutImageProcessor''']
  
  
  if TYPE_CHECKING:
         from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
         from .processing_donut import DonutProcessor
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_donut_swin import (
                    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DonutSwinModel,
                    DonutSwinPreTrainedModel,
                )
  
         try:
                if not is_vision_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .feature_extraction_donut import DonutFeatureExtractor
                from .image_processing_donut import DonutImageProcessor
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  