




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a__		     =     logging.get_logger(__name__)





def 				A__ (snake_case   :		str				)     ->						YolosConfig:
					__UpperCamelCase					:	Dict             =      YolosConfig()

					# size of the architecture
					if "yolos_ti" in yolos_name:
										__UpperCamelCase					:	Union[str, Any]             =      1_92
										__UpperCamelCase					:	Optional[int]             =      7_68
										__UpperCamelCase					:	List[Any]             =      12
										__UpperCamelCase					:	Optional[int]             =      3
										__UpperCamelCase					:	Optional[Any]             =      [8_00, 13_33]
										__UpperCamelCase					:	Optional[Any]             =      False
					elif yolos_name == "yolos_s_dWr":
										__UpperCamelCase					:	Union[str, Any]             =      3_30
										__UpperCamelCase					:	List[Any]             =      14
										__UpperCamelCase					:	Dict             =      6
										__UpperCamelCase					:	Optional[int]             =      13_20
					elif "yolos_s" in yolos_name:
										__UpperCamelCase					:	Dict             =      3_84
										__UpperCamelCase					:	Optional[int]             =      15_36
										__UpperCamelCase					:	Optional[int]             =      12
										__UpperCamelCase					:	List[str]             =      6
					elif "yolos_b" in yolos_name:
										__UpperCamelCase					:	List[Any]             =      [8_00, 13_44]

					__UpperCamelCase					:	Optional[Any]             =      91
					__UpperCamelCase					:	Union[str, Any]             =      """huggingface/label-files"""
					__UpperCamelCase					:	List[Any]             =      """coco-detection-id2label.json"""
					__UpperCamelCase					:	Any             =      json.load(open(hf_hub_download(snake_case		,	snake_case		,	repo_type="""dataset"""				)		,	"""r"""				)				)
					__UpperCamelCase					:	Tuple             =      {int(snake_case				): v for k, v in idalabel.items()}
					__UpperCamelCase					:	str             =      idalabel
					__UpperCamelCase					:	List[Any]             =      {v: k for k, v in idalabel.items()}

					return config





def 				A__ (snake_case   :		dict		,	snake_case   :		YolosConfig		,	snake_case   :		bool = False				)     ->						List[Any]:
					for i in range(config.num_hidden_layers				):
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										__UpperCamelCase					:	Tuple             =      state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
										__UpperCamelCase					:	str             =      state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
										# next, add query, keys and values (in that order) to the state dict
										__UpperCamelCase					:	List[Any]             =      in_proj_weight[: config.hidden_size, :]
										__UpperCamelCase					:	Union[str, Any]             =      in_proj_bias[: config.hidden_size]
										__UpperCamelCase					:	Union[str, Any]             =      in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__UpperCamelCase					:	Tuple             =      in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										__UpperCamelCase					:	Tuple             =      in_proj_weight[-config.hidden_size :, :]
										__UpperCamelCase					:	List[Any]             =      in_proj_bias[-config.hidden_size :]





def 				A__ (snake_case   :		str				)     ->						str:
					if "backbone" in name:
										__UpperCamelCase					:	str             =      name.replace("""backbone"""		,	"""vit"""				)
					if "cls_token" in name:
										__UpperCamelCase					:	Any             =      name.replace("""cls_token"""		,	"""embeddings.cls_token"""				)
					if "det_token" in name:
										__UpperCamelCase					:	Tuple             =      name.replace("""det_token"""		,	"""embeddings.detection_tokens"""				)
					if "mid_pos_embed" in name:
										__UpperCamelCase					:	int             =      name.replace("""mid_pos_embed"""		,	"""encoder.mid_position_embeddings"""				)
					if "pos_embed" in name:
										__UpperCamelCase					:	int             =      name.replace("""pos_embed"""		,	"""embeddings.position_embeddings"""				)
					if "patch_embed.proj" in name:
										__UpperCamelCase					:	Tuple             =      name.replace("""patch_embed.proj"""		,	"""embeddings.patch_embeddings.projection"""				)
					if "blocks" in name:
										__UpperCamelCase					:	Any             =      name.replace("""blocks"""		,	"""encoder.layer"""				)
					if "attn.proj" in name:
										__UpperCamelCase					:	Dict             =      name.replace("""attn.proj"""		,	"""attention.output.dense"""				)
					if "attn" in name:
										__UpperCamelCase					:	str             =      name.replace("""attn"""		,	"""attention.self"""				)
					if "norm1" in name:
										__UpperCamelCase					:	Union[str, Any]             =      name.replace("""norm1"""		,	"""layernorm_before"""				)
					if "norm2" in name:
										__UpperCamelCase					:	Optional[int]             =      name.replace("""norm2"""		,	"""layernorm_after"""				)
					if "mlp.fc1" in name:
										__UpperCamelCase					:	Tuple             =      name.replace("""mlp.fc1"""		,	"""intermediate.dense"""				)
					if "mlp.fc2" in name:
										__UpperCamelCase					:	Union[str, Any]             =      name.replace("""mlp.fc2"""		,	"""output.dense"""				)
					if "class_embed" in name:
										__UpperCamelCase					:	Tuple             =      name.replace("""class_embed"""		,	"""class_labels_classifier"""				)
					if "bbox_embed" in name:
										__UpperCamelCase					:	Dict             =      name.replace("""bbox_embed"""		,	"""bbox_predictor"""				)
					if "vit.norm" in name:
										__UpperCamelCase					:	Dict             =      name.replace("""vit.norm"""		,	"""vit.layernorm"""				)

					return name





def 				A__ (snake_case   :		dict		,	snake_case   :		YolosForObjectDetection				)     ->						dict:
					for key in orig_state_dict.copy().keys():
										__UpperCamelCase					:	Dict             =      orig_state_dict.pop(snake_case				)

										if "qkv" in key:
															__UpperCamelCase					:	Optional[int]             =      key.split("""."""				)
															__UpperCamelCase					:	Union[str, Any]             =      int(key_split[2]				)
															__UpperCamelCase					:	Any             =      model.vit.encoder.layer[layer_num].attention.attention.all_head_size
															if "weight" in key:
																				__UpperCamelCase					:	Tuple             =      val[:dim, :]
																				__UpperCamelCase					:	str             =      val[
																				    dim : dim * 2, :
																				]
																				__UpperCamelCase					:	int             =      val[-dim:, :]
															else:
																				__UpperCamelCase					:	Tuple             =      val[:dim]
																				__UpperCamelCase					:	Any             =      val[dim : dim * 2]
																				__UpperCamelCase					:	str             =      val[-dim:]
										else:
															__UpperCamelCase					:	Optional[int]             =      val

					return orig_state_dict





def 				A__ ()     ->						torch.Tensor:
					__UpperCamelCase					:	Any             =      """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__UpperCamelCase					:	Any             =      Image.open(requests.get(snake_case		,	stream=snake_case				).raw				)
					return im





@torch.no_grad()
def 				A__ (snake_case   :		str		,	snake_case   :		str		,	snake_case   :		str		,	snake_case   :		bool = False				)     ->						Optional[Any]:
					__UpperCamelCase					:	List[str]             =      get_yolos_config(snake_case				)

					# load original state_dict
					__UpperCamelCase					:	Tuple             =      torch.load(snake_case		,	map_location="""cpu"""				)["""model"""]

					# load 🤗 model
					__UpperCamelCase					:	Dict             =      YolosForObjectDetection(snake_case				)
					model.eval()
					__UpperCamelCase					:	int             =      convert_state_dict(snake_case		,	snake_case				)
					model.load_state_dict(snake_case				)

					# Check outputs on an image, prepared by YolosImageProcessor
					__UpperCamelCase					:	Any             =      8_00 if yolos_name != """yolos_ti""" else 5_12
					__UpperCamelCase					:	int             =      YolosImageProcessor(format="""coco_detection"""		,	size=snake_case				)
					__UpperCamelCase					:	Any             =      image_processor(images=prepare_img()		,	return_tensors="""pt"""				)
					__UpperCamelCase					:	List[Any]             =      model(**snake_case				)
					__UpperCamelCase      ,			__UpperCamelCase					:	Optional[int]             =      outputs.logits, outputs.pred_boxes

					__UpperCamelCase      ,			__UpperCamelCase					:	List[Any]             =      None, None
					if yolos_name == "yolos_ti":
										__UpperCamelCase					:	Dict             =      torch.tensor(
										    [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]				)
										__UpperCamelCase					:	Union[str, Any]             =      torch.tensor(
										    [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]				)
					elif yolos_name == "yolos_s_200_pre":
										__UpperCamelCase					:	Optional[Any]             =      torch.tensor(
										    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]				)
										__UpperCamelCase					:	str             =      torch.tensor(
										    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]				)
					elif yolos_name == "yolos_s_300_pre":
										__UpperCamelCase					:	Union[str, Any]             =      torch.tensor(
										    [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]				)
										__UpperCamelCase					:	Tuple             =      torch.tensor(
										    [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]				)
					elif yolos_name == "yolos_s_dWr":
										__UpperCamelCase					:	str             =      torch.tensor(
										    [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]				)
										__UpperCamelCase					:	Optional[Any]             =      torch.tensor(
										    [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]				)
					elif yolos_name == "yolos_base":
										__UpperCamelCase					:	Optional[int]             =      torch.tensor(
										    [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]				)
										__UpperCamelCase					:	List[str]             =      torch.tensor(
										    [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]				)
					else:
										raise ValueError(F'''Unknown yolos_name: {yolos_name}'''				)

					assert torch.allclose(logits[0, :3, :3]		,	snake_case		,	atol=1e-4				)
					assert torch.allclose(pred_boxes[0, :3, :3]		,	snake_case		,	atol=1e-4				)

					Path(snake_case				).mkdir(exist_ok=snake_case				)
					print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''				)
					model.save_pretrained(snake_case				)
					print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
					image_processor.save_pretrained(snake_case				)

					if push_to_hub:
										__UpperCamelCase					:	Tuple             =      {
										    """yolos_ti""": """yolos-tiny""",
										    """yolos_s_200_pre""": """yolos-small""",
										    """yolos_s_300_pre""": """yolos-small-300""",
										    """yolos_s_dWr""": """yolos-small-dwr""",
										    """yolos_base""": """yolos-base""",
										}

										print("""Pushing to the hub..."""				)
										__UpperCamelCase					:	int             =      model_mapping[yolos_name]
										image_processor.push_to_hub(snake_case		,	organization="""hustvl"""				)
										model.push_to_hub(snake_case		,	organization="""hustvl"""				)


if __name__ == "__main__":
	a__		     =     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--yolos_name''',
	    default='''yolos_s_200_pre''',
	    type=str,
	    help=(
	        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
	        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
	    ),
	)
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)

	a__		     =     parser.parse_args()
	convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
	from transformers import LlamaTokenizerFast
except ImportError as e:
	warnings.warn(e)
	warnings.warn(
	    '''The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'''
	)
	a__		     =     None
a__		     =     {
    '''7B''': 1_1008,
    '''13B''': 1_3824,
    '''30B''': 1_7920,
    '''65B''': 2_2016,
    '''70B''': 2_8672,
}
a__		     =     {
    '''7B''': 1,
    '''7Bf''': 1,
    '''13B''': 2,
    '''13Bf''': 2,
    '''30B''': 4,
    '''65B''': 8,
    '''70B''': 8,
    '''70Bf''': 8,
}





def 				A__ (snake_case   :		str		,	snake_case   :		Optional[Any]=1		,	snake_case   :		Optional[int]=2_56				)     ->						Dict:
					return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3				)				) + multiple_of - 1) // multiple_of)





def 				A__ (snake_case   :		Dict				)     ->						Optional[Any]:
					with open(snake_case		,	"""r"""				) as f:
										return json.load(snake_case				)





def 				A__ (snake_case   :		List[Any]		,	snake_case   :		Any				)     ->						Dict:
					with open(snake_case		,	"""w"""				) as f:
										json.dump(snake_case		,	snake_case				)





def 				A__ (snake_case   :		Tuple		,	snake_case   :		List[Any]		,	snake_case   :		List[str]		,	snake_case   :		str=True				)     ->						int:
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					__UpperCamelCase					:	Optional[int]             =      os.path.join(snake_case		,	"""tmp"""				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)

					__UpperCamelCase					:	Any             =      read_json(os.path.join(snake_case		,	"""params.json"""				)				)
					__UpperCamelCase					:	Dict             =      NUM_SHARDS[model_size]
					__UpperCamelCase					:	List[Any]             =      params["""n_layers"""]
					__UpperCamelCase					:	Any             =      params["""n_heads"""]
					__UpperCamelCase					:	Optional[int]             =      n_heads // num_shards
					__UpperCamelCase					:	Tuple             =      params["""dim"""]
					__UpperCamelCase					:	Optional[int]             =      dim // n_heads
					__UpperCamelCase					:	Dict             =      10000.0
					__UpperCamelCase					:	str             =      1.0 / (base ** (torch.arange(0		,	snake_case		,	2				).float() / dims_per_head))

					if "n_kv_heads" in params:
										__UpperCamelCase					:	Any             =      params["""n_kv_heads"""]  # for GQA / MQA
										__UpperCamelCase					:	Optional[int]             =      n_heads_per_shard // num_key_value_heads
										__UpperCamelCase					:	List[Any]             =      dim // num_key_value_heads
					else:  # compatibility with other checkpoints
										__UpperCamelCase					:	Optional[Any]             =      n_heads
										__UpperCamelCase					:	Any             =      n_heads_per_shard
										__UpperCamelCase					:	str             =      dim

					# permute for sliced rotary
					def permute(snake_case   :		Tuple		,	snake_case   :		Dict=n_heads		,	snake_case   :		str=dim		,	snake_case   :		Optional[Any]=dim				):
										return w.view(snake_case		,	dima // n_heads // 2		,	2		,	snake_case				).transpose(1		,	2				).reshape(snake_case		,	snake_case				)

					print(F'''Fetching all parameters from the checkpoint at {input_base_path}.'''				)
					# Load weights
					if model_size == "7B":
										# Not sharded
										# (The sharded implementation would also work, but this is simpler.)
										__UpperCamelCase					:	Any             =      torch.load(os.path.join(snake_case		,	"""consolidated.00.pth"""				)		,	map_location="""cpu"""				)
					else:
										# Sharded
										__UpperCamelCase					:	Optional[int]             =      [
										    torch.load(os.path.join(snake_case		,	F'''consolidated.{i:02d}.pth'''				)		,	map_location="""cpu"""				)
										    for i in range(snake_case				)
										]
					__UpperCamelCase					:	Optional[int]             =      0
					__UpperCamelCase					:	List[str]             =      {"""weight_map""": {}}
					for layer_i in range(snake_case				):
										__UpperCamelCase					:	Tuple             =      F'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
										if model_size == "7B":
															# Unsharded
															__UpperCamelCase					:	Optional[Any]             =      {
															    F'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
															        loaded[F'''layers.{layer_i}.attention.wq.weight''']				),
															    F'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
															        loaded[F'''layers.{layer_i}.attention.wk.weight''']				),
															    F'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[F'''layers.{layer_i}.attention.wv.weight'''],
															    F'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[F'''layers.{layer_i}.attention.wo.weight'''],
															    F'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w1.weight'''],
															    F'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w2.weight'''],
															    F'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w3.weight'''],
															    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[F'''layers.{layer_i}.attention_norm.weight'''],
															    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[F'''layers.{layer_i}.ffn_norm.weight'''],
															}
										else:
															# Sharded
															# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
															# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
															# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

															__UpperCamelCase					:	int             =      {
															    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
															        F'''layers.{layer_i}.attention_norm.weight'''
															    ].clone(),
															    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
															        F'''layers.{layer_i}.ffn_norm.weight'''
															    ].clone(),
															}
															__UpperCamelCase					:	Union[str, Any]             =      permute(
															    torch.cat(
															        [
															            loaded[i][F'''layers.{layer_i}.attention.wq.weight'''].view(snake_case		,	snake_case		,	snake_case				)
															            for i in range(snake_case				)
															        ]		,	dim=0		,	).reshape(snake_case		,	snake_case				)				)
															__UpperCamelCase					:	int             =      permute(
															    torch.cat(
															        [
															            loaded[i][F'''layers.{layer_i}.attention.wk.weight'''].view(
															                snake_case		,	snake_case		,	snake_case				)
															            for i in range(snake_case				)
															        ]		,	dim=0		,	).reshape(snake_case		,	snake_case				)		,	snake_case		,	snake_case		,	snake_case		,	)
															__UpperCamelCase					:	Dict             =      torch.cat(
															    [
															        loaded[i][F'''layers.{layer_i}.attention.wv.weight'''].view(
															            snake_case		,	snake_case		,	snake_case				)
															        for i in range(snake_case				)
															    ]		,	dim=0		,	).reshape(snake_case		,	snake_case				)

															__UpperCamelCase					:	str             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.attention.wo.weight'''] for i in range(snake_case				)]		,	dim=1				)
															__UpperCamelCase					:	Dict             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(snake_case				)]		,	dim=0				)
															__UpperCamelCase					:	Dict             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(snake_case				)]		,	dim=1				)
															__UpperCamelCase					:	List[str]             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(snake_case				)]		,	dim=0				)

										__UpperCamelCase					:	Dict             =      inv_freq
										for k, v in state_dict.items():
															__UpperCamelCase					:	str             =      filename
															param_count += v.numel()
										torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)

					__UpperCamelCase					:	Dict             =      F'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
					if model_size == "7B":
										# Unsharded
										__UpperCamelCase					:	Dict             =      {
										    """model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
										    """model.norm.weight""": loaded["""norm.weight"""],
										    """lm_head.weight""": loaded["""output.weight"""],
										}
					else:
										__UpperCamelCase					:	Dict             =      {
										    """model.norm.weight""": loaded[0]["""norm.weight"""],
										    """model.embed_tokens.weight""": torch.cat(
										        [loaded[i]["""tok_embeddings.weight"""] for i in range(snake_case				)]		,	dim=1				),
										    """lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(snake_case				)]		,	dim=0				),
										}

					for k, v in state_dict.items():
										__UpperCamelCase					:	str             =      filename
										param_count += v.numel()
					torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)

					# Write configs
					__UpperCamelCase					:	Union[str, Any]             =      {"""total_size""": param_count * 2}
					write_json(snake_case		,	os.path.join(snake_case		,	"""pytorch_model.bin.index.json"""				)				)
					__UpperCamelCase					:	str             =      params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
					__UpperCamelCase					:	str             =      params["""multiple_of"""] if """multiple_of""" in params else 2_56
					__UpperCamelCase					:	str             =      LlamaConfig(
					    hidden_size=snake_case		,	intermediate_size=compute_intermediate_size(snake_case		,	snake_case		,	snake_case				)		,	num_attention_heads=params["""n_heads"""]		,	num_hidden_layers=params["""n_layers"""]		,	rms_norm_eps=params["""norm_eps"""]		,	num_key_value_heads=snake_case		,	)
					config.save_pretrained(snake_case				)

					# Make space so we can load the model properly now.
					del state_dict
					del loaded
					gc.collect()

					print("""Loading the checkpoint in a Llama model."""				)
					__UpperCamelCase					:	Optional[int]             =      LlamaForCausalLM.from_pretrained(snake_case		,	torch_dtype=torch.floataa		,	low_cpu_mem_usage=snake_case				)
					# Avoid saving this as part of the config.
					del model.config._name_or_path

					print("""Saving in the Transformers format."""				)
					model.save_pretrained(snake_case		,	safe_serialization=snake_case				)
					shutil.rmtree(snake_case				)





def 				A__ (snake_case   :		Any		,	snake_case   :		str				)     ->						Union[str, Any]:
					# Initialize the tokenizer based on the `spm` model
					__UpperCamelCase					:	Union[str, Any]             =      LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
					print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''				)
					__UpperCamelCase					:	Tuple             =      tokenizer_class(snake_case				)
					tokenizer.save_pretrained(snake_case				)





def 				A__ ()     ->						int:
					__UpperCamelCase					:	Union[str, Any]             =      argparse.ArgumentParser()
					parser.add_argument(
					    """--input_dir"""		,	help="""Location of LLaMA weights, which contains tokenizer.model and model folders"""		,	)
					parser.add_argument(
					    """--model_size"""		,	choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""]		,	)
					parser.add_argument(
					    """--output_dir"""		,	help="""Location to write HF model and tokenizer"""		,	)
					parser.add_argument("""--safe_serialization"""		,	type=snake_case		,	help="""Whether or not to save using `safetensors`."""				)
					__UpperCamelCase					:	str             =      parser.parse_args()
					if args.model_size != "tokenizer_only":
										write_model(
										    model_path=args.output_dir		,	input_base_path=os.path.join(args.input_dir		,	args.model_size				)		,	model_size=args.model_size		,	safe_serialization=args.safe_serialization		,	)
					__UpperCamelCase					:	int             =      os.path.join(args.input_dir		,	"""tokenizer.model"""				)
					write_tokenizer(args.output_dir		,	snake_case				)


if __name__ == "__main__":
	main()






