code: 
  
  
  
  
  
  
  import argparse
  import os
  from os.path import isfile
  
  import torch
  
  from transformers import GPTaConfig
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase=0      ):
  
  
      '''simple docstring'''
  
  
  
      if name is None:
          _lowercase     =	None
      else:
          _lowercase     =	'.' * max(0,      spaces - 2      ) + '# {:' + str(50 - spaces      ) + 's}'
          _lowercase     =	fmt.format(_lowercase      )
  
      # Print and recurse (if needed).
      if isinstance(_lowercase,      _lowercase      ):
          if msg is not None:
              print(_lowercase      )
          for k in val.keys():
              recursive_print(_lowercase,      val[k],      spaces + 2      )
      elif isinstance(_lowercase,      torch.Tensor      ):
          print(_lowercase,      ':',      val.size()      )
      else:
          print(_lowercase,      ':',      _lowercase      )
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	param.size()
      # other versions store [num_heads * num_splits * hidden_size, :]
      _lowercase     =	(num_heads, num_splits, hidden_size) + input_shape[1:]
      _lowercase     =	param.view(*_lowercase      )
      _lowercase     =	param.transpose(0,      1      ).contiguous()
      _lowercase     =	param.view(*_lowercase      )
      return param
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	config.n_positions
      _lowercase     =	config.n_layer
      _lowercase     =	config.vocab_size
      _lowercase     =	config.n_head
      _lowercase     =	config.n_embd // config.n_head
  
      _lowercase     =	sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
      _lowercase     =	{
          'transformer.wte.weight': word_embeddings,
          'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
          'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
          'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
      }
  
      _lowercase     =	'model.language_model.encoder.layers.'
      for i in range(_lowercase      ):
          _lowercase     =	torch.tril(torch.ones((n_positions, n_positions),      dtype=torch.bool      )      )
          _lowercase     =	causal_mask.view(1,      1,      _lowercase,      _lowercase      )
          _lowercase     =	causal_mask
          _lowercase     =	torch.tensor(-1e4,      dtype=torch.bfloataa      )
  
          _lowercase     =	sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
          _lowercase     =	sd_megatron[f'''{pf}{i}.input_layernorm.bias''']
  
          _lowercase     =	sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
          _lowercase     =	fix_query_key_value_ordering(_lowercase,      3,      _lowercase,      _lowercase      )
          _lowercase     =	vala.transpose(0,      1      ).contiguous()
  
          _lowercase     =	sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
          _lowercase     =	fix_query_key_value_ordering(_lowercase,      3,      _lowercase,      _lowercase      )
          _lowercase     =	vala
  
          _lowercase     =	sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
              0,      1      )
          _lowercase     =	sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
          _lowercase     =	sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
          _lowercase     =	sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
          _lowercase     =	sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0,      1      )
          _lowercase     =	sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
          _lowercase     =	sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
              0,      1      )
          _lowercase     =	sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']
  
      # For LM head, transformers' wants the matrix to weight embeddings.
      _lowercase     =	word_embeddings
  
      return sd_hf
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	64000
      _lowercase     =	config_megatron['encoder_seq_length']
      _lowercase     =	config_megatron['hidden_size']
      _lowercase     =	config_megatron['num_layers']
      _lowercase     =	config_megatron['num_attention_heads']
      _lowercase     =	config_megatron['ffn_hidden_size']
      _lowercase     =	'gelu'
      _lowercase     =	0.1
      _lowercase     =	0.1
      _lowercase     =	0.1
      _lowercase     =	config_megatron['layernorm_epsilon']  # 1e-5
      _lowercase     =	config_megatron['init_method_std']  # 0.02
      _lowercase     =	config_megatron['apply_query_key_layer_scaling']  # True
      _lowercase     =	True
      _lowercase     =	True
  
      # This identifies the 6.7B (7B) model which uses a different tokenizer
      if config_megatron["hidden_size"] == 4096:
          _lowercase     =	1  # <|endoftext|>
          _lowercase     =	1  # <|endoftext|>
          _lowercase     =	0  # <unk>
      else:
          _lowercase     =	2  # <s>
          _lowercase     =	3  # <|endoftext|>
          _lowercase     =	0  # <pad>
  
      return config_hf
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      print(_lowercase      )
  
      _lowercase     =	args.checkpoint_path
      _lowercase     =	args.save_path
      if isfile(_lowercase      ):
          raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''      )
  
      # Load the model.
      _lowercase     =	torch.load(_lowercase,      map_location='cpu'      )
  
      # Load the config.
      _lowercase     =	checkpoint['hyper_parameters']['cfg']
      _lowercase     =	GPTaConfig()
      _lowercase     =	copy_config(config_hf=_lowercase,      config_megatron=_lowercase      )
      _lowercase     =	['GPT2LMHeadModel']
  
      _lowercase     =	checkpoint['state_dict']
  
      # Convert.
      print('Converting'      )
      _lowercase     =	convert_megatron_checkpoint(_lowercase,      _lowercase      )
  
      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
          recursive_print(_lowercase,      _lowercase      )
  
      _lowercase     =	'GPTSw3Tokenizer'
  
      # Store the config to file.
      print('Saving config'      )
      config_hf.save_pretrained(_lowercase      )
  
      # Store the state_dict to file.
      _lowercase     =	os.path.join(_lowercase,      'pytorch_model.bin'      )
      print(f'''Saving checkpoint to "{output_checkpoint_file}"'''      )
      torch.save(_lowercase,      _lowercase      )
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  argparse.ArgumentParser()
     parser.add_argument(
         '''--checkpoint_path''',
         type=str,
         required=True,
         help='''e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000''',
     )
     parser.add_argument('''--save_path''', type=str, required=True, help='''e.g. /home/user/gpt-sw3/hf''')
     parser.add_argument('''--print-checkpoint-structure''', action='''store_true''')
     lowerCamelCase										=  parser.parse_args()
     main(_args)
  
style_context: 
  
  
  
  
  
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import (
      BeitConfig,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitImageProcessor,
  )
  from transformers.image_utils import PILImageResampling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase										=  logging.get_logger(__name__)
  def        UpperCAmelCase      (   _lowercase,      _lowercase=False,      _lowercase=False      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	'backbone.' if is_semantic else ''
  
      _lowercase     =	[]
      for i in range(config.num_hidden_layers      ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')      )
          rename_keys.append(
              (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')      )
          rename_keys.append(
              (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')      )
          rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')      )
  
      # projection layer + position embeddings
      rename_keys.extend(
          [
              (f'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
              (f'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
              (f'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
          ]      )
  
      if has_lm_head:
          # mask token + shared relative position bias + layernorm
          rename_keys.extend(
              [
                  ('mask_token', 'beit.embeddings.mask_token'),
                  (
                      'rel_pos_bias.relative_position_bias_table',
                      'beit.encoder.relative_position_bias.relative_position_bias_table',
                  ),
                  (
                      'rel_pos_bias.relative_position_index',
                      'beit.encoder.relative_position_bias.relative_position_index',
                  ),
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
              ]      )
      elif is_semantic:
          # semantic segmentation classification heads
          rename_keys.extend(
              [
                  ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
                  ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
                  ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
                  ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
              ]      )
      else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
                  ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]      )
  
      return rename_keys
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase=False,      _lowercase=False      ):
  
  
      '''simple docstring'''
  
  
  
      for i in range(config.num_hidden_layers      ):
          _lowercase     =	'backbone.' if is_semantic else ''
          # queries, keys and values
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''      )
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''      )
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''      )
  
          _lowercase     =	in_proj_weight[
              : config.hidden_size, :
          ]
          _lowercase     =	q_bias
          _lowercase     =	in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          _lowercase     =	in_proj_weight[
              -config.hidden_size :, :
          ]
          _lowercase     =	v_bias
  
          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''      )
          _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''      )
  
          _lowercase     =	gamma_a
          _lowercase     =	gamma_a
  
          # relative_position bias table + index
          if not has_lm_head:
              # each layer has its own relative position bias
              _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''      )
              _lowercase     =	state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''      )
  
              _lowercase     =	table
              _lowercase     =	index
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	dct.pop(_lowercase      )
      _lowercase     =	val
  def        UpperCAmelCase      (   ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	'http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase     =	Image.open(requests.get(_lowercase,      stream=_lowercase      ).raw      )
      return im
  @torch.no_grad()
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	BeitConfig()
      _lowercase     =	False
      _lowercase     =	False
      _lowercase     =	'huggingface/label-files'
      # set config parameters based on URL
      if checkpoint_url[-9:-4] == "pt22k":
          # masked image modeling
          _lowercase     =	True
          _lowercase     =	True
          _lowercase     =	True
      elif checkpoint_url[-9:-4] == "ft22k":
          # intermediate fine-tuning on ImageNet-22k
          _lowercase     =	True
          _lowercase     =	21841
          _lowercase     =	'imagenet-22k-id2label.json'
          _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9205]
          del idalabel[15027]
          _lowercase     =	idalabel
          _lowercase     =	{v: k for k, v in idalabel.items()}
      elif checkpoint_url[-8:-4] == "to1k":
          # fine-tuning on ImageNet-1k
          _lowercase     =	True
          _lowercase     =	1000
          _lowercase     =	'imagenet-1k-id2label.json'
          _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
          _lowercase     =	idalabel
          _lowercase     =	{v: k for k, v in idalabel.items()}
          if "384" in checkpoint_url:
              _lowercase     =	384
          if "512" in checkpoint_url:
              _lowercase     =	512
      elif "ade20k" in checkpoint_url:
          # fine-tuning
          _lowercase     =	True
          _lowercase     =	150
          _lowercase     =	'ade20k-id2label.json'
          _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
          _lowercase     =	idalabel
          _lowercase     =	{v: k for k, v in idalabel.items()}
          _lowercase     =	640
          _lowercase     =	True
      else:
          raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''      )
  
      # size of the architecture
      if "base" in checkpoint_url:
          pass
      elif "large" in checkpoint_url:
          _lowercase     =	1024
          _lowercase     =	4096
          _lowercase     =	24
          _lowercase     =	16
          if "ade20k" in checkpoint_url:
              _lowercase     =	640
              _lowercase     =	[7, 11, 15, 23]
      else:
          raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'      )
  
      # load state_dict of original model, remove and rename some keys
      _lowercase     =	torch.hub.load_state_dict_from_url(_lowercase,      map_location='cpu',      check_hash=_lowercase      )
      _lowercase     =	state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']
  
      _lowercase     =	create_rename_keys(_lowercase,      has_lm_head=_lowercase,      is_semantic=_lowercase      )
      for src, dest in rename_keys:
          rename_key(_lowercase,      _lowercase,      _lowercase      )
      read_in_q_k_v(_lowercase,      _lowercase,      has_lm_head=_lowercase,      is_semantic=_lowercase      )
      if is_semantic:
          # add prefix to decoder keys
          for key, val in state_dict.copy().items():
              _lowercase     =	state_dict.pop(_lowercase      )
              if key.startswith('backbone.fpn'      ):
                  _lowercase     =	key.replace('backbone.fpn',      'fpn'      )
              _lowercase     =	val
  
      # load HuggingFace model
      if checkpoint_url[-9:-4] == "pt22k":
          _lowercase     =	BeitForMaskedImageModeling(_lowercase      )
      elif "ade20k" in checkpoint_url:
          _lowercase     =	BeitForSemanticSegmentation(_lowercase      )
      else:
          _lowercase     =	BeitForImageClassification(_lowercase      )
      model.eval()
      model.load_state_dict(_lowercase      )
  
      # Check outputs on an image
      if is_semantic:
          _lowercase     =	BeitImageProcessor(size=config.image_size,      do_center_crop=_lowercase      )
          _lowercase     =	load_dataset('hf-internal-testing/fixtures_ade20k',      split='test'      )
          _lowercase     =	Image.open(ds[0]['file']      )
      else:
          _lowercase     =	BeitImageProcessor(
              size=config.image_size,      resample=PILImageResampling.BILINEAR,      do_center_crop=_lowercase      )
          _lowercase     =	prepare_img()
  
      _lowercase     =	image_processor(images=_lowercase,      return_tensors='pt'      )
      _lowercase     =	encoding['pixel_values']
  
      _lowercase     =	model(_lowercase      )
      _lowercase     =	outputs.logits
  
      # verify logits
      _lowercase     =	torch.Size([1, 1000]      )
      if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'      ):
          _lowercase     =	torch.Size([1, 196, 8192]      )
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'      ):
          _lowercase     =	torch.Size([1, 196, 8192]      )
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'      ):
          _lowercase     =	torch.Size([1, 21841]      )
          _lowercase     =	torch.tensor([2.2_288, 2.4_671, 0.7_395]      )
          _lowercase     =	2397
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'      ):
          _lowercase     =	torch.Size([1, 21841]      )
          _lowercase     =	torch.tensor([1.6_881, -0.2_787, 0.5_901]      )
          _lowercase     =	2396
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'      ):
          _lowercase     =	torch.tensor([0.1_241, 0.0_798, -0.6_569]      )
          _lowercase     =	285
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-1.2_385, -1.0_987, -1.0_108]      )
          _lowercase     =	281
      elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-1.5_303, -0.9_484, -0.3_147]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'      ):
          _lowercase     =	torch.tensor([0.4_610, -0.0_928, 0.2_086]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-0.4_804, 0.6_257, -0.1_837]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-0.3_062, 0.7_261, 0.4_852]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'      ):
          _lowercase     =	(1, 150, 160, 160)
          _lowercase     =	torch.tensor(
              [
                  [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                  [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                  [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
              ]      )
      elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'      ):
          _lowercase     =	(1, 150, 160, 160)
          _lowercase     =	torch.tensor(
              [
                  [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
                  [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
                  [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
              ]      )
      else:
          raise ValueError('Can\'t verify logits as model is not supported'      )
  
      assert logits.shape == expected_shape, "Shape of logits not as expected"
      if not has_lm_head:
          if is_semantic:
              assert torch.allclose(
                  logits[0, :3, :3, :3],      _lowercase,      atol=1e-3      ), "First elements of logits not as expected"
          else:
              print('Predicted class idx:',      logits.argmax(-1      ).item()      )
              assert torch.allclose(
                  logits[0, :3],      _lowercase,      atol=1e-3      ), "First elements of logits not as expected"
              assert logits.argmax(-1      ).item() == expected_class_idx, "Predicted class index not as expected"
  
      Path(_lowercase      ).mkdir(exist_ok=_lowercase      )
      print(f'''Saving model to {pytorch_dump_folder_path}'''      )
      model.save_pretrained(_lowercase      )
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''      )
      image_processor.save_pretrained(_lowercase      )
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  argparse.ArgumentParser()
  
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
         type=str,
         help='''URL to the original PyTorch checkpoint (.pth file).''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
     )
     lowerCamelCase										=  parser.parse_args()
     convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  