code: 
  
  
  import argparse
  import json
  
  import gdown
  import numpy as np
  import torch
  from huggingface_hub import hf_hub_download
  
  from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       TimesformerConfig()
  
       if "large" in model_name:
            snake_case					=       96
  
       if "hr" in model_name:
            snake_case					=       16
            snake_case					=       4_48
  
       snake_case					=       '''huggingface/label-files'''
       if "k400" in model_name:
            snake_case					=       4_00
            snake_case					=       '''kinetics400-id2label.json'''
       elif "k600" in model_name:
            snake_case					=       6_00
            snake_case					=       '''kinetics600-id2label.json'''
       elif "ssv2" in model_name:
            snake_case					=       1_74
            snake_case					=       '''something-something-v2-id2label.json'''
       else:
            raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''       )
       snake_case					=       json.load(open(hf_hub_download(UpperCamelCase_							,UpperCamelCase_							,repo_type='''dataset'''       )							,'''r'''       )       )
       snake_case					=       {int(UpperCamelCase_       ): v for k, v in idalabel.items()}
       snake_case					=       idalabel
       snake_case					=       {v: k for k, v in idalabel.items()}
  
       return config
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if "encoder." in name:
            snake_case					=       name.replace('''encoder.'''							,''''''       )
       if "cls_token" in name:
            snake_case					=       name.replace('''cls_token'''							,'''timesformer.embeddings.cls_token'''       )
       if "pos_embed" in name:
            snake_case					=       name.replace('''pos_embed'''							,'''timesformer.embeddings.position_embeddings'''       )
       if "time_embed" in name:
            snake_case					=       name.replace('''time_embed'''							,'''timesformer.embeddings.time_embeddings'''       )
       if "patch_embed.proj" in name:
            snake_case					=       name.replace('''patch_embed.proj'''							,'''timesformer.embeddings.patch_embeddings.projection'''       )
       if "patch_embed.norm" in name:
            snake_case					=       name.replace('''patch_embed.norm'''							,'''timesformer.embeddings.norm'''       )
       if "blocks" in name:
            snake_case					=       name.replace('''blocks'''							,'''timesformer.encoder.layer'''       )
       if "attn.proj" in name:
            snake_case					=       name.replace('''attn.proj'''							,'''attention.output.dense'''       )
       if "attn" in name and "bias" not in name and "temporal" not in name:
            snake_case					=       name.replace('''attn'''							,'''attention.self'''       )
       if "attn" in name and "temporal" not in name:
            snake_case					=       name.replace('''attn'''							,'''attention.attention'''       )
       if "temporal_norm1" in name:
            snake_case					=       name.replace('''temporal_norm1'''							,'''temporal_layernorm'''       )
       if "temporal_attn.proj" in name:
            snake_case					=       name.replace('''temporal_attn'''							,'''temporal_attention.output.dense'''       )
       if "temporal_fc" in name:
            snake_case					=       name.replace('''temporal_fc'''							,'''temporal_dense'''       )
       if "norm1" in name and "temporal" not in name:
            snake_case					=       name.replace('''norm1'''							,'''layernorm_before'''       )
       if "norm2" in name:
            snake_case					=       name.replace('''norm2'''							,'''layernorm_after'''       )
       if "mlp.fc1" in name:
            snake_case					=       name.replace('''mlp.fc1'''							,'''intermediate.dense'''       )
       if "mlp.fc2" in name:
            snake_case					=       name.replace('''mlp.fc2'''							,'''output.dense'''       )
       if "norm.weight" in name and "fc" not in name and "temporal" not in name:
            snake_case					=       name.replace('''norm.weight'''							,'''timesformer.layernorm.weight'''       )
       if "norm.bias" in name and "fc" not in name and "temporal" not in name:
            snake_case					=       name.replace('''norm.bias'''							,'''timesformer.layernorm.bias'''       )
       if "head" in name:
            snake_case					=       name.replace('''head'''							,'''classifier'''       )
  
       return name
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for key in orig_state_dict.copy().keys():
            snake_case					=       orig_state_dict.pop(UpperCamelCase_       )
  
            if key.startswith('''model.'''       ):
                 snake_case					=       key.replace('''model.'''							,''''''       )
  
            if "qkv" in key:
                 snake_case					=       key.split('''.'''       )
                 snake_case					=       int(key_split[1]       )
                 snake_case					=       '''timesformer.encoder.layer.'''
                 if "temporal" in key:
                      snake_case					=       '''.temporal_attention.attention.qkv.'''
                 else:
                      snake_case					=       '''.attention.attention.qkv.'''
                 if "weight" in key:
                      snake_case					=       val
                 else:
                      snake_case					=       val
            else:
                 snake_case					=       val
  
       return orig_state_dict
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       hf_hub_download(
           repo_id='''hf-internal-testing/spaghetti-video'''							,filename='''eating_spaghetti.npy'''							,repo_type='''dataset'''       )
       snake_case					=       np.load(UpperCamelCase_       )
       return list(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       get_timesformer_config(UpperCamelCase_       )
  
       snake_case					=       TimesformerForVideoClassification(UpperCamelCase_       )
  
       # download original checkpoint, hosted on Google Drive
       snake_case					=       '''pytorch_model.bin'''
       gdown.cached_download(UpperCamelCase_							,UpperCamelCase_							,quiet=UpperCamelCase_       )
       snake_case					=       torch.load(UpperCamelCase_							,map_location='''cpu'''       )
       if "model" in files:
            snake_case					=       files['''model''']
       elif "module" in files:
            snake_case					=       files['''module''']
       else:
            snake_case					=       files['''model_state''']
       snake_case					=       convert_state_dict(UpperCamelCase_							,UpperCamelCase_       )
  
       model.load_state_dict(UpperCamelCase_       )
       model.eval()
  
       # verify model on basic input
       snake_case					=       VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,image_std=[0.5, 0.5, 0.5]       )
       snake_case					=       prepare_video()
       snake_case					=       image_processor(video[:8]							,return_tensors='''pt'''       )
  
       snake_case					=       model(**UpperCamelCase_       )
       snake_case					=       outputs.logits
  
       snake_case					=       [
           # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
           '''timesformer-base-finetuned-k400''',
           '''timesformer-large-finetuned-k400''',
           '''timesformer-hr-finetuned-k400''',
           # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
           '''timesformer-base-finetuned-k600''',
           '''timesformer-large-finetuned-k600''',
           '''timesformer-hr-finetuned-k600''',
           # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
           '''timesformer-base-finetuned-ssv2''',
           '''timesformer-large-finetuned-ssv2''',
           '''timesformer-hr-finetuned-ssv2''',
       ]
  
       # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
       if model_name == "timesformer-base-finetuned-k400":
            snake_case					=       torch.Size([1, 4_00]       )
            snake_case					=       torch.tensor([-0.3016, -0.7713, -0.4205]       )
       elif model_name == "timesformer-base-finetuned-k600":
            snake_case					=       torch.Size([1, 6_00]       )
            snake_case					=       torch.tensor([-0.7267, -0.7466, 3.2404]       )
       elif model_name == "timesformer-base-finetuned-ssv2":
            snake_case					=       torch.Size([1, 1_74]       )
            snake_case					=       torch.tensor([-0.9059, 0.6433, -3.1457]       )
       elif model_name == "timesformer-large-finetuned-k400":
            snake_case					=       torch.Size([1, 4_00]       )
            snake_case					=       torch.tensor([0, 0, 0]       )
       elif model_name == "timesformer-large-finetuned-k600":
            snake_case					=       torch.Size([1, 6_00]       )
            snake_case					=       torch.tensor([0, 0, 0]       )
       elif model_name == "timesformer-large-finetuned-ssv2":
            snake_case					=       torch.Size([1, 1_74]       )
            snake_case					=       torch.tensor([0, 0, 0]       )
       elif model_name == "timesformer-hr-finetuned-k400":
            snake_case					=       torch.Size([1, 4_00]       )
            snake_case					=       torch.tensor([-0.9617, -3.7311, -3.7708]       )
       elif model_name == "timesformer-hr-finetuned-k600":
            snake_case					=       torch.Size([1, 6_00]       )
            snake_case					=       torch.tensor([2.5273, 0.7127, 1.8848]       )
       elif model_name == "timesformer-hr-finetuned-ssv2":
            snake_case					=       torch.Size([1, 1_74]       )
            snake_case					=       torch.tensor([-3.6756, -0.7513, 0.7180]       )
       else:
            raise ValueError(F'''Model name not supported. Should be one of {model_names}'''       )
  
       # verify logits
       assert logits.shape == expected_shape
       assert torch.allclose(logits[0, :3]							,UpperCamelCase_							,atol=1e-4       )
       print('''Logits ok!'''       )
  
       if pytorch_dump_folder_path is not None:
            print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''       )
            image_processor.save_pretrained(UpperCamelCase_       )
            model.save_pretrained(UpperCamelCase_       )
  
       if push_to_hub:
            print('''Pushing to the hub...'''       )
            model.push_to_hub(F'''fcakyon/{model_name}'''       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--checkpoint_url",
       default="https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download",
       type=str,
       help=(
           "URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"
           " download link."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default="",
       type=str,
       help="Path to the output PyTorch model directory.",
   )
   parser.add_argument("--model_name", default="timesformer-base-finetuned-k400", type=str, help="Name of the model.")
   parser.add_argument(
       "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
   )
  
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
   convert_timesformer_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
   )
  
  
  
  
  
style_context: 
  
  
  import argparse
  import json
  import os
  
  import fairseq
  import torch
  from fairseq.data import Dictionary
  
  # Register SEW's fairseq modules
  from sew_asapp import tasks  # noqa: F401
  
  from transformers import (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  _SCREAMING_SNAKE_CASE    :				List[str]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					{
      "post_extract_proj": "feature_projection",
      "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
      "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
      "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
      "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
      "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
      "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
      "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
      "output.dense": "encoder.encoder.layer.*.output.dense",
      "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
      "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
      "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
      "encoder.upsample.0": "encoder.upsample.projection",
      "encoder.layer_norm": "encoder.layer_norm",
      "w2v_model.layer_norm": "layer_norm",
      "w2v_encoder.proj": "lm_head",
      "mask_emb": "masked_spec_embed",
  }
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for attribute in key.split('''.'''       ):
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       )
  
       if weight_type is not None:
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       ).shape
       else:
            snake_case					=       hf_pointer.shape
  
       assert hf_shape == value.shape, (
           F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
           F''' {value.shape} for {full_name}'''
       )
  
       if weight_type == "weight":
            snake_case					=       value
       elif weight_type == "weight_g":
            snake_case					=       value
       elif weight_type == "weight_v":
            snake_case					=       value
       elif weight_type == "bias":
            snake_case					=       value
       else:
            snake_case					=       value
  
       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       fairseq_model.state_dict()
  
       snake_case					=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
       for name, value in fairseq_dict.items():
            snake_case					=       False
            if "conv_layers" in name:
                 load_conv_layer(
                     UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,hf_model.config.feat_extract_norm == '''group'''							,)
                 snake_case					=       True
            else:
                 for key, mapped_key in MAPPING.items():
                      snake_case					=       '''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
  
                      if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
                           snake_case					=       True
                           if "*" in mapped_key:
                                snake_case					=       name.split(UpperCamelCase_       )[0].split('''.'''       )[-2]
                                if not layer_index.isnumeric():
                                     continue
                                snake_case					=       mapped_key.replace('''*'''							,UpperCamelCase_       )
                           if "weight_g" in name:
                                snake_case					=       '''weight_g'''
                           elif "weight_v" in name:
                                snake_case					=       '''weight_v'''
                           elif "weight" in name:
                                snake_case					=       '''weight'''
                           elif "bias" in name:
                                snake_case					=       '''bias'''
                           else:
                                snake_case					=       None
                           set_recursively(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
                      continue
            if not is_used:
                 unused_weights.append(UpperCamelCase_       )
  
       logger.warning(F'''Unused weights: {unused_weights}'''       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       full_name.split('''conv_layers.'''       )[-1]
       snake_case					=       name.split('''.'''       )
       snake_case					=       int(items[0]       )
       snake_case					=       int(items[1]       )
  
       if type_id == 0:
            if "bias" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                     F'''{full_name} has size {value.shape}, but'''
                     F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                 )
                 snake_case					=       value
                 logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
            elif "weight" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                     F'''{full_name} has size {value.shape}, but'''
                     F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                 )
                 snake_case					=       value
                 logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''       )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
            if "bias" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                     F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                     " found."
                 )
                 snake_case					=       value
                 logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
            elif "weight" in name:
                 assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                     F'''{full_name} has size {value.shape}, but'''
                     F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                 )
                 snake_case					=       value
                 logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
       else:
            unused_weights.append(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       SEWDConfig()
       if is_finetuned:
            snake_case					=       model.wav_encoder.wav_model.cfg
       else:
            snake_case					=       model.cfg
  
       snake_case					=       fs_config.conv_bias
       snake_case					=       eval(fs_config.conv_feature_layers       )
       snake_case					=       [x[0] for x in conv_layers]
       snake_case					=       [x[1] for x in conv_layers]
       snake_case					=       [x[2] for x in conv_layers]
       snake_case					=       '''gelu'''
       snake_case					=       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       snake_case					=       0.0
       snake_case					=       fs_config.activation_fn.name
       snake_case					=       fs_config.encoder_embed_dim
       snake_case					=       0.02
       snake_case					=       fs_config.encoder_ffn_embed_dim
       snake_case					=       1e-5
       snake_case					=       fs_config.encoder_layerdrop
       snake_case					=       fs_config.encoder_attention_heads
       snake_case					=       fs_config.conv_pos_groups
       snake_case					=       fs_config.conv_pos
       snake_case					=       len(UpperCamelCase_       )
       snake_case					=       fs_config.encoder_layers
       snake_case					=       fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       snake_case					=       fs_config.max_position_embeddings
       snake_case					=       fs_config.position_buckets
       snake_case					=       fs_config.share_att_key
       snake_case					=       fs_config.relative_attention
       snake_case					=       fs_config.position_biased_input
       snake_case					=       tuple(fs_config.pos_att_type.split('''|'''       )       )
       snake_case					=       fs_config.norm_rel_ebd
  
       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
            snake_case					=       model.cfg
            snake_case					=       fs_config.final_dropout
            snake_case					=       fs_config.layerdrop
       snake_case					=       fs_config.activation_dropout
       snake_case					=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       snake_case					=       fs_config.attention_dropout
       snake_case					=       fs_config.dropout_input
       snake_case					=       fs_config.dropout
       snake_case					=       fs_config.mask_channel_length
       snake_case					=       fs_config.mask_channel_prob
       snake_case					=       fs_config.mask_length
       snake_case					=       fs_config.mask_prob
  
       snake_case					=       '''Wav2Vec2FeatureExtractor'''
       snake_case					=       '''Wav2Vec2CTCTokenizer'''
  
       return config
  @torch.no_grad()
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if is_finetuned:
            snake_case				,						snake_case				,						snake_case					=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]							,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
       else:
            snake_case				,						snake_case				,						snake_case					=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )
  
       if config_path is not None:
            snake_case					=       SEWDConfig.from_pretrained(UpperCamelCase_       )
       else:
            snake_case					=       convert_config(model[0]							,UpperCamelCase_       )
       snake_case					=       model[0].eval()
  
       snake_case					=       True if config.feat_extract_norm == '''layer''' else False
       snake_case					=       WavaVecaFeatureExtractor(
           feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)
  
       if is_finetuned:
            if dict_path:
                 snake_case					=       Dictionary.load(UpperCamelCase_       )
  
                 # important change bos & pad token id since CTC symbol is <pad> and
                 # not <s> as in fairseq
                 snake_case					=       target_dict.pad_index
                 snake_case					=       target_dict.bos_index
                 snake_case					=       target_dict.pad_index
                 snake_case					=       target_dict.bos_index
                 snake_case					=       target_dict.eos_index
                 snake_case					=       len(target_dict.symbols       )
                 snake_case					=       os.path.join(UpperCamelCase_							,'''vocab.json'''       )
                 if not os.path.isdir(UpperCamelCase_       ):
                      logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_       )       )
                      return
                 os.makedirs(UpperCamelCase_							,exist_ok=UpperCamelCase_       )
                 with open(UpperCamelCase_							,'''w'''							,encoding='''utf-8'''       ) as vocab_handle:
                      json.dump(target_dict.indices							,UpperCamelCase_       )
                 snake_case					=       WavaVecaCTCTokenizer(
                     UpperCamelCase_							,unk_token=target_dict.unk_word							,pad_token=target_dict.pad_word							,bos_token=target_dict.bos_word							,eos_token=target_dict.eos_word							,word_delimiter_token='''|'''							,do_lower_case=UpperCamelCase_							,)
                 snake_case					=       WavaVecaProcessor(feature_extractor=UpperCamelCase_							,tokenizer=UpperCamelCase_       )
                 processor.save_pretrained(UpperCamelCase_       )
  
            snake_case					=       SEWDForCTC(UpperCamelCase_       )
       else:
            snake_case					=       SEWDModel(UpperCamelCase_       )
            feature_extractor.save_pretrained(UpperCamelCase_       )
  
       recursively_load_weights(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       hf_model.save_pretrained(UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				List[Any]       =					argparse.ArgumentParser()
   parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
   parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
   parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
   parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
   parser.add_argument(
       "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
   )
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )
  
  
  
  
  