

"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a__			:						Optional[int]							=			logging.get_logger(__name__)





def 					__UpperCamelCase (     )					->      Optional[Any]:




					"""simple docstring"""



					__lowercase        =      DetaConfig(
					    num_queries=9_00			,						encoder_ffn_dim=20_48			,						decoder_ffn_dim=20_48			,						num_feature_levels=5			,						assign_first_stage=a__			,						with_box_refine=a__			,						two_stage=a__			,						)

					# set labels
					__lowercase        =      91
					__lowercase        =      '''huggingface/label-files'''
					__lowercase        =      '''coco-detection-id2label.json'''
					__lowercase        =      json.load(open(cached_download(hf_hub_url(a__			,						a__			,						repo_type='''dataset'''      )      )			,						'''r'''      )      )
					__lowercase        =      {int(a__      ): v for k, v in idalabel.items()}
					__lowercase        =      idalabel
					__lowercase        =      {v: k for k, v in idalabel.items()}

					return config





def 					__UpperCamelCase (     a__:			Optional[int]      )					->      Union[str, Any]:




					"""simple docstring"""



					__lowercase        =      []

					# stem
					# fmt: off
					rename_keys.append(('''backbone.0.body.conv1.weight''', '''model.backbone.model.embedder.embedder.convolution.weight''')      )
					rename_keys.append(('''backbone.0.body.bn1.weight''', '''model.backbone.model.embedder.embedder.normalization.weight''')      )
					rename_keys.append(('''backbone.0.body.bn1.bias''', '''model.backbone.model.embedder.embedder.normalization.bias''')      )
					rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''model.backbone.model.embedder.embedder.normalization.running_mean''')      )
					rename_keys.append(('''backbone.0.body.bn1.running_var''', '''model.backbone.model.embedder.embedder.normalization.running_var''')      )
					# stages
					for stage_idx in range(len(config.backbone_config.depths      )      ):
										for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
															# shortcut
															if layer_idx == 0:
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
																				    )      )
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
																				    )      )
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
																				    )      )
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
																				    )      )
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
																				    )      )
															# 3 convs
															for i in range(3      ):
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
																				    )      )
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
																				    )      )
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
																				    )      )
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
																				    )      )
																				rename_keys.append(
																				    (
																				        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
																				        f'''model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
																				    )      )
    # transformer encoder
					for i in range(config.encoder_layers      ):
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')      )
										rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')      )

					# transformer decoder
					for i in range(config.decoder_layers      ):
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')      )
										rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')      )

					# fmt: on

					return rename_keys





def 					__UpperCamelCase (     a__:			str			,						a__:			str			,						a__:			List[str]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      dct.pop(a__      )
					__lowercase        =      val





def 					__UpperCamelCase (     a__:			int			,						a__:			Any      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      config.d_model
					for i in range(config.decoder_layers      ):
										# read in weights + bias of input projection layer of self-attention
										__lowercase        =      state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''      )
										__lowercase        =      state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''      )
										# next, add query, keys and values (in that order) to the state dict
										__lowercase        =      in_proj_weight[:hidden_size, :]
										__lowercase        =      in_proj_bias[:hidden_size]
										__lowercase        =      in_proj_weight[
										    hidden_size : hidden_size * 2, :
										]
										__lowercase        =      in_proj_bias[hidden_size : hidden_size * 2]
										__lowercase        =      in_proj_weight[-hidden_size:, :]
										__lowercase        =      in_proj_bias[-hidden_size:]





def 					__UpperCamelCase (     )					->      Optional[Any]:




					"""simple docstring"""



					__lowercase        =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					__lowercase        =      Image.open(requests.get(a__			,						stream=a__      ).raw      )

					return im





@torch.no_grad()
def 					__UpperCamelCase (     a__:			Tuple			,						a__:			Dict			,						a__:			Optional[Any]      )					->      int:




					"""simple docstring"""



					__lowercase        =      get_deta_config()

					# load original state dict
					if model_name == "deta-resnet-50":
										__lowercase        =      '''adet_checkpoint0011.pth'''
					elif model_name == "deta-resnet-50-24-epochs":
										__lowercase        =      '''adet_2x_checkpoint0023.pth'''
					else:
										raise ValueError(f'''Model name {model_name} not supported'''      )
					__lowercase        =      hf_hub_download(repo_id='''nielsr/deta-checkpoints'''			,						filename=a__      )
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )['''model''']

					# rename keys
					__lowercase        =      create_rename_keys(a__      )
					for src, dest in rename_keys:
										rename_key(a__			,						a__			,						a__      )
					read_in_decoder_q_k_v(a__			,						a__      )

					# fix some prefixes
					for key in state_dict.copy().keys():
										if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
															__lowercase        =      state_dict.pop(a__      )
															__lowercase        =      val
										if "input_proj" in key:
															__lowercase        =      state_dict.pop(a__      )
															__lowercase        =      val
										if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
															__lowercase        =      state_dict.pop(a__      )
															__lowercase        =      val

    # finally, create HuggingFace model and load state dict
					__lowercase        =      DetaForObjectDetection(a__      )
					model.load_state_dict(a__      )
					model.eval()

					__lowercase        =      '''cuda''' if torch.cuda.is_available() else '''cpu'''
					model.to(a__      )

					# load image processor
					__lowercase        =      DetaImageProcessor(format='''coco_detection'''      )

					# verify our conversion on image
					__lowercase        =      prepare_img()
					__lowercase        =      processor(images=a__			,						return_tensors='''pt'''      )
					__lowercase        =      encoding['''pixel_values''']
					__lowercase        =      model(pixel_values.to(a__      )      )

					# verify logits
					if model_name == "deta-resnet-50":
										__lowercase        =      torch.tensor(
										    [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]      )
										__lowercase        =      torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]      )
					elif model_name == "deta-resnet-50-24-epochs":
										__lowercase        =      torch.tensor(
										    [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]      )
										__lowercase        =      torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]      )

					assert torch.allclose(outputs.logits[0, :3, :3]			,						expected_logits.to(a__      )			,						atol=1e-4      )
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]			,						expected_boxes.to(a__      )			,						atol=1e-4      )
					print('''Everything ok!'''      )

					if pytorch_dump_folder_path:
										# Save model and processor
										logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''      )
										Path(a__      ).mkdir(exist_ok=a__      )
										model.save_pretrained(a__      )
										processor.save_pretrained(a__      )

					# Push to hub
					if push_to_hub:
										print('''Pushing model and processor to hub...'''      )
										model.push_to_hub(f'''jozhang97/{model_name}'''      )
										processor.push_to_hub(f'''jozhang97/{model_name}'''      )


if __name__ == "__main__":
							a__			:						List[str]							=			argparse.ArgumentParser()

							parser.add_argument(
							    """--model_name""",
							    type=str,
							    default="""deta-resnet-50""",
							    choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
							    help="""Name of the model you'd like to convert.""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=str,
							    help="""Path to the folder to output PyTorch model.""",
							)
							parser.add_argument(
							    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
							)
							a__			:						int							=			parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			int			,						a__:			Dict=0      )					->      Any:




					"""simple docstring"""



					if name is None:
										__lowercase        =      None
					else:
										__lowercase        =      '''.''' * max(0			,						spaces - 2      ) + '''# {:''' + str(50 - spaces      ) + '''s}'''
										__lowercase        =      fmt.format(a__      )

					# Print and recurse (if needed).
					if isinstance(a__			,						a__      ):
										if msg is not None:
															print(a__      )
										for k in val.keys():
															recursive_print(a__			,						val[k]			,						spaces + 2      )
					elif isinstance(a__			,						torch.Tensor      ):
										print(a__			,						''':'''			,						val.size()      )
					else:
										print(a__			,						''':'''			,						a__      )





def 					__UpperCamelCase (     a__:			Optional[int]			,						a__:			Tuple			,						a__:			List[Any]			,						a__:			List[str]      )					->      str:




					"""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(*a__      )
					__lowercase        =      param.transpose(0			,						1      ).contiguous()
					__lowercase        =      param.view(*a__      )
					return param





def 					__UpperCamelCase (     a__:			Tuple			,						a__:			Optional[int]      )					->      List[str]:




					"""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(a__      ):
										__lowercase        =      torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool      )      )
										__lowercase        =      causal_mask.view(1			,						1			,						a__			,						a__      )
										__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(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala.transpose(0			,						1      ).contiguous()

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__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 (     a__:			Any			,						a__:			List[Any]      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      6_40_00
					__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"] == 40_96:
										__lowercase        =      1  # <|endoftext|>
										__lowercase        =      1  # <|endoftext|>
										__lowercase        =      0  # <unk>
					else:
										__lowercase        =      2  # <s>
										__lowercase        =      3  # <|endoftext|>
										__lowercase        =      0  # <pad>

					return config_hf





def 					__UpperCamelCase (     a__:			Dict      )					->      Union[str, Any]:




					"""simple docstring"""



					print(a__      )

					__lowercase        =      args.checkpoint_path
					__lowercase        =      args.save_path
					if isfile(a__      ):
										raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''      )

					# Load the model.
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )

					# Load the config.
					__lowercase        =      checkpoint['''hyper_parameters''']['''cfg''']
					__lowercase        =      GPTaConfig()
					__lowercase        =      copy_config(config_hf=a__			,						config_megatron=a__      )
					__lowercase        =      ['''GPT2LMHeadModel''']

					__lowercase        =      checkpoint['''state_dict''']

					# Convert.
					print('''Converting'''      )
					__lowercase        =      convert_megatron_checkpoint(a__			,						a__      )

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(a__			,						a__      )

					__lowercase        =      '''GPTSw3Tokenizer'''

					# Store the config to file.
					print('''Saving config'''      )
					config_hf.save_pretrained(a__      )

					# Store the state_dict to file.
					__lowercase        =      os.path.join(a__			,						'''pytorch_model.bin'''      )
					print(f'''Saving checkpoint to "{output_checkpoint_file}"'''      )
					torch.save(a__			,						a__      )


if __name__ == "__main__":
							a__			:						Union[str, Any]							=			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""")
							a__			:						Dict							=			parser.parse_args()
							main(_args)


