



'''simple docstring'''






import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__:   List[str]								=			logging.get_logger(__name__)


def  snake_case_     (					_lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Optional[int]=False  )      ->   str:
							UpperCAmelCase :       List[Any] 							=		[]
							for i in range(config.num_hidden_layers  ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")  )
														rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")  )
														rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")  )
														rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")  )
														rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")  )
														rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")  )
														rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")  )
														rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")  )
														rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")  )
														rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")  )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('''cls_token''', '''deit.embeddings.cls_token'''),
							        ('''dist_token''', '''deit.embeddings.distillation_token'''),
							        ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
							        ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
							        ('''pos_embed''', '''deit.embeddings.position_embeddings'''),
							    ]  )

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''layernorm.weight'''),
														        ('''norm.bias''', '''layernorm.bias'''),
														        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
														        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
														    ]  )

														# if just the base model, we should remove "deit" from all keys that start with "deit"
														UpperCAmelCase :       Union[str, Any] 							=		[(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''  ) else pair for pair in rename_keys]
							else:
														# layernorm + classification heads
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''deit.layernorm.weight'''),
														        ('''norm.bias''', '''deit.layernorm.bias'''),
														        ('''head.weight''', '''cls_classifier.weight'''),
														        ('''head.bias''', '''cls_classifier.bias'''),
														        ('''head_dist.weight''', '''distillation_classifier.weight'''),
														        ('''head_dist.bias''', '''distillation_classifier.bias'''),
														    ]  )

							return rename_keys


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						List[Any]=False  )      ->   Dict:
							for i in range(config.num_hidden_layers  ):
														if base_model:
																					UpperCAmelCase :       Optional[Any] 							=		''''''
														else:
																					UpperCAmelCase :       Dict 							=		'''deit.'''
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														UpperCAmelCase :       List[Any] 							=		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[str] 							=		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 :       List[Any] 							=		in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCAmelCase :       Optional[int] 							=		in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCAmelCase :       List[Any] 							=		in_proj_bias[-config.hidden_size :]


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Tuple  )      ->   Optional[int]:
							UpperCAmelCase :       Optional[int] 							=		dct.pop(_lowerCAmelCase  )
							UpperCAmelCase :       List[str] 							=		val


def  snake_case_     (					)      ->   str:
							UpperCAmelCase :       Union[str, Any] 							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase :       Union[str, Any] 							=		Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase  ).raw  )
							return im


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						int  )      ->   List[Any]:
							UpperCAmelCase :       Union[str, Any] 							=		DeiTConfig()
							# all deit models have fine-tuned heads
							UpperCAmelCase :       str 							=		False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							UpperCAmelCase :       int 							=		1000
							UpperCAmelCase :       List[str] 							=		'''huggingface/label-files'''
							UpperCAmelCase :       Union[str, Any] 							=		'''imagenet-1k-id2label.json'''
							UpperCAmelCase :       Dict 							=		json.load(open(hf_hub_download(_lowerCAmelCase   ,      _lowerCAmelCase   ,      repo_type='''dataset'''  )   ,      '''r'''  )  )
							UpperCAmelCase :       Dict 							=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
							UpperCAmelCase :       Union[str, Any] 							=		idalabel
							UpperCAmelCase :       List[Any] 							=		{v: k for k, v in idalabel.items()}
							UpperCAmelCase :       Union[str, Any] 							=		int(deit_name[-6:-4]  )
							UpperCAmelCase :       Optional[int] 							=		int(deit_name[-3:]  )
							# size of the architecture
							if deit_name[9:].startswith('''tiny'''  ):
														UpperCAmelCase :       Tuple 							=		192
														UpperCAmelCase :       List[Any] 							=		768
														UpperCAmelCase :       Optional[Any] 							=		12
														UpperCAmelCase :       Dict 							=		3
							elif deit_name[9:].startswith('''small'''  ):
														UpperCAmelCase :       int 							=		384
														UpperCAmelCase :       str 							=		1536
														UpperCAmelCase :       str 							=		12
														UpperCAmelCase :       List[Any] 							=		6
							if deit_name[9:].startswith('''base'''  ):
														pass
							elif deit_name[4:].startswith('''large'''  ):
														UpperCAmelCase :       List[Any] 							=		1024
														UpperCAmelCase :       Any 							=		4096
														UpperCAmelCase :       Optional[int] 							=		24
														UpperCAmelCase :       int 							=		16

							# load original model from timm
							UpperCAmelCase :       Optional[int] 							=		timm.create_model(_lowerCAmelCase   ,      pretrained=_lowerCAmelCase  )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							UpperCAmelCase :       Optional[Any] 							=		timm_model.state_dict()
							UpperCAmelCase :       Any 							=		create_rename_keys(_lowerCAmelCase   ,      _lowerCAmelCase  )
							for src, dest in rename_keys:
														rename_key(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
							read_in_q_k_v(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							# load HuggingFace model
							UpperCAmelCase :       List[Any] 							=		DeiTForImageClassificationWithTeacher(_lowerCAmelCase  ).eval()
							model.load_state_dict(_lowerCAmelCase  )

							# Check outputs on an image, prepared by DeiTImageProcessor
							UpperCAmelCase :       List[Any] 							=		int(
							    (256 / 224) * config.image_size  )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							UpperCAmelCase :       int 							=		DeiTImageProcessor(size=_lowerCAmelCase   ,      crop_size=config.image_size  )
							UpperCAmelCase :       List[Any] 							=		image_processor(images=prepare_img()   ,      return_tensors='''pt'''  )
							UpperCAmelCase :       List[Any] 							=		encoding['''pixel_values''']
							UpperCAmelCase :       int 							=		model(_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		timm_model(_lowerCAmelCase  )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCAmelCase   ,      outputs.logits   ,      atol=1e-3  )

							Path(_lowerCAmelCase  ).mkdir(exist_ok=_lowerCAmelCase  )
							print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""  )
							model.save_pretrained(_lowerCAmelCase  )
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
							image_processor.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
						UpperCamelCase__:   List[Any]								=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--deit_name",
						    default="vit_deit_base_distilled_patch16_224",
						    type=str,
						    help="Name of the DeiT timm model you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)

						UpperCamelCase__:   List[str]								=			parser.parse_args()
						convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)










'''simple docstring'''






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"
						)
						UpperCamelCase__:   str								=			None
UpperCamelCase__:   int								=			{
    "7B": 11008,
    "13B": 13824,
    "30B": 17920,
    "65B": 22016,
    "70B": 28672,
}
UpperCamelCase__:   List[Any]								=			{
    "7B": 1,
    "7Bf": 1,
    "13B": 2,
    "13Bf": 2,
    "30B": 4,
    "65B": 8,
    "70B": 8,
    "70Bf": 8,
}


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Tuple=1   ,      _lowerCAmelCase  :						List[Any]=256  )      ->   Optional[Any]:
							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3  )  ) + multiple_of - 1) // multiple_of)


def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   str:
							with open(_lowerCAmelCase   ,      '''r'''  ) as f:
														return json.load(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Any  )      ->   Optional[Any]:
							with open(_lowerCAmelCase   ,      '''w'''  ) as f:
														json.dump(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Any=True  )      ->   List[Any]:
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )
							UpperCAmelCase :       int 							=		os.path.join(_lowerCAmelCase   ,      '''tmp'''  )
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		read_json(os.path.join(_lowerCAmelCase   ,      '''params.json'''  )  )
							UpperCAmelCase :       str 							=		NUM_SHARDS[model_size]
							UpperCAmelCase :       Any 							=		params['''n_layers''']
							UpperCAmelCase :       str 							=		params['''n_heads''']
							UpperCAmelCase :       Any 							=		n_heads // num_shards
							UpperCAmelCase :       List[str] 							=		params['''dim''']
							UpperCAmelCase :       Optional[Any] 							=		dim // n_heads
							UpperCAmelCase :       str 							=		1_0_0_0_0.0
							UpperCAmelCase :       Optional[int] 							=		1.0 / (base ** (torch.arange(0   ,      _lowerCAmelCase   ,      2  ).float() / dims_per_head))

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

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

							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 :       int 							=		torch.load(os.path.join(_lowerCAmelCase   ,      '''consolidated.00.pth'''  )   ,      map_location='''cpu'''  )
							else:
														# Sharded
														UpperCAmelCase :       Optional[Any] 							=		[
														    torch.load(os.path.join(_lowerCAmelCase   ,      f"""consolidated.{i:02d}.pth"""  )   ,      map_location='''cpu'''  )
														    for i in range(_lowerCAmelCase  )
														]
							UpperCAmelCase :       Any 							=		0
							UpperCAmelCase :       str 							=		{'''weight_map''': {}}
							for layer_i in range(_lowerCAmelCase  ):
														UpperCAmelCase :       Optional[Any] 							=		f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"""
														if model_size == "7B":
																					# Unsharded
																					UpperCAmelCase :       Optional[int] 							=		{
																					    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 :       List[str] 							=		{
																					    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(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					            for i in range(_lowerCAmelCase  )
																					        ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )  )
																					UpperCAmelCase :       Optional[Any] 							=		permute(
																					    torch.cat(
																					        [
																					            loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view(
																					                _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					            for i in range(_lowerCAmelCase  )
																					        ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      )
																					UpperCAmelCase :       str 							=		torch.cat(
																					    [
																					        loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view(
																					            _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					        for i in range(_lowerCAmelCase  )
																					    ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )

																					UpperCAmelCase :       Optional[int] 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=1  )
																					UpperCAmelCase :       Any 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=0  )
																					UpperCAmelCase :       str 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=1  )
																					UpperCAmelCase :       Tuple 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=0  )

														UpperCAmelCase :       Any 							=		inv_freq
														for k, v in state_dict.items():
																					UpperCAmelCase :       List[Any] 							=		filename
																					param_count += v.numel()
														torch.save(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )  )

							UpperCAmelCase :       Optional[int] 							=		f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"""
							if model_size == "7B":
														# Unsharded
														UpperCAmelCase :       str 							=		{
														    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
														    '''model.norm.weight''': loaded['''norm.weight'''],
														    '''lm_head.weight''': loaded['''output.weight'''],
														}
							else:
														UpperCAmelCase :       Any 							=		{
														    '''model.norm.weight''': loaded[0]['''norm.weight'''],
														    '''model.embed_tokens.weight''': torch.cat(
														        [loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase  )]   ,      dim=1  ),
														    '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase  )]   ,      dim=0  ),
														}

							for k, v in state_dict.items():
														UpperCAmelCase :       Optional[int] 							=		filename
														param_count += v.numel()
							torch.save(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )  )

							# Write configs
							UpperCAmelCase :       Union[str, Any] 							=		{'''total_size''': param_count * 2}
							write_json(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      '''pytorch_model.bin.index.json'''  )  )
							UpperCAmelCase :       int 							=		params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
							UpperCAmelCase :       Tuple 							=		params['''multiple_of'''] if '''multiple_of''' in params else 256
							UpperCAmelCase :       Any 							=		LlamaConfig(
							    hidden_size=_lowerCAmelCase   ,      intermediate_size=compute_intermediate_size(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )   ,      num_attention_heads=params['''n_heads''']   ,      num_hidden_layers=params['''n_layers''']   ,      rms_norm_eps=params['''norm_eps''']   ,      num_key_value_heads=_lowerCAmelCase   ,      )
							config.save_pretrained(_lowerCAmelCase  )

							# 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(_lowerCAmelCase   ,      torch_dtype=torch.floataa   ,      low_cpu_mem_usage=_lowerCAmelCase  )
							# Avoid saving this as part of the config.
							del model.config._name_or_path

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


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   List[str]:
							# Initialize the tokenizer based on the `spm` model
							UpperCAmelCase :       Dict 							=		LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}."""  )
							UpperCAmelCase :       List[Any] 							=		tokenizer_class(_lowerCAmelCase  )
							tokenizer.save_pretrained(_lowerCAmelCase  )


def  snake_case_     (					)      ->   List[Any]:
							UpperCAmelCase :       int 							=		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=_lowerCAmelCase   ,      help='''Whether or not to save using `safetensors`.'''  )
							UpperCAmelCase :       List[Any] 							=		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 :       Optional[int] 							=		os.path.join(args.input_dir   ,      '''tokenizer.model'''  )
							write_tokenizer(args.output_dir   ,      _lowerCAmelCase  )


if __name__ == "__main__":
						main()





