






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  int	,			__magic_name__					:  int					)		->			Tuple:






	"""simple docstring"""



	return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  Tuple	,			__magic_name__					:  Any="attention"					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :str          =	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]					)
	UpperCamelCase     :Optional[Any]          =	k_tmp.reshape(k_tmp.shape[0]	,			k_tmp.shape[1] * k_tmp.shape[2]					)
	UpperCamelCase     :Dict          =	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]					)
	UpperCamelCase     :List[str]          =	o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]	,			o_tmp.shape[2]					)
	UpperCamelCase     :List[str]          =	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]					)
	UpperCamelCase     :int          =	q_tmp.reshape(q_tmp.shape[0]	,			q_tmp.shape[1] * q_tmp.shape[2]					)
	UpperCamelCase     :Optional[Any]          =	np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]					)
	UpperCamelCase     :int          =	v_tmp.reshape(v_tmp.shape[0]	,			v_tmp.shape[1] * v_tmp.shape[2]					)
	return k, o, q, v

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Optional[int]	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  Any=False					)		->			Tuple:






	"""simple docstring"""



	if split_mlp_wi:
		UpperCamelCase     :Optional[Any]          =	params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
		UpperCamelCase     :List[str]          =	params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
		UpperCamelCase     :Dict          =	(wi_a, wi_a)
	else:
		UpperCamelCase     :Union[str, Any]          =	params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

	UpperCamelCase     :Tuple          =	params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
	return wi, wo

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Dict	,			__magic_name__					:  List[Any]	,			__magic_name__					:  List[str]					)		->			List[str]:






	"""simple docstring"""



	return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  dict	,			*, __magic_name__					:  int	,			__magic_name__					:  bool	,			__magic_name__					:  bool = False					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	traverse_util.flatten_dict(variables["""target"""]					)
	UpperCamelCase     :int          =	{"""/""".join(__magic_name__					): v for k, v in old.items()}

	# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
	UpperCamelCase     :str          =	"""encoder/encoder/mlp/wi_0/kernel""" in old
	print("""Split MLP:"""	,			__magic_name__					)

	UpperCamelCase     :Optional[int]          =	collections.OrderedDict()

	# Shared embeddings.
	UpperCamelCase     :Dict          =	old["""token_embedder/embedding"""]

	# Encoder.
	for i in range(__magic_name__					):
		# Block i, layer 0 (Self Attention).
		UpperCamelCase     :Optional[int]          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			"""pre_attention_layer_norm"""					)
		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	tax_attention_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			"""attention"""					)
		UpperCamelCase     :Union[str, Any]          =	layer_norm
		UpperCamelCase     :Tuple          =	k.T
		UpperCamelCase     :Optional[Any]          =	o.T
		UpperCamelCase     :Dict          =	q.T
		UpperCamelCase     :List[str]          =	v.T

		# Block i, layer 1 (MLP).
		UpperCamelCase     :Optional[Any]          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			"""pre_mlp_layer_norm"""					)
		UpperCamelCase     ,      UpperCamelCase     :Dict          =	tax_mlp_lookup(__magic_name__	,			__magic_name__	,			"""encoder"""	,			__magic_name__					)
		UpperCamelCase     :List[Any]          =	layer_norm
		if split_mlp_wi:
			UpperCamelCase     :Any          =	wi[0].T
			UpperCamelCase     :Optional[Any]          =	wi[1].T
		else:
			UpperCamelCase     :Optional[int]          =	wi.T
		UpperCamelCase     :Any          =	wo.T
		if scalable_attention:
			# convert the rel_embedding of each layer
			UpperCamelCase     :Optional[Any]          =	tax_relpos_bias_lookup(
			    __magic_name__	,			__magic_name__	,			"""encoder"""					).T

	UpperCamelCase     :Tuple          =	old["""encoder/encoder_norm/scale"""]

	if not scalable_attention:
		UpperCamelCase     :Dict          =	tax_relpos_bias_lookup(
		    __magic_name__	,			0	,			"""encoder"""					).T
		UpperCamelCase     :Tuple          =	tax_relpos_bias_lookup(
		    __magic_name__	,			0	,			"""decoder"""					).T

	if not is_encoder_only:
		# Decoder.
		for i in range(__magic_name__					):
			# Block i, layer 0 (Self Attention).
			UpperCamelCase     :Dict          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""pre_self_attention_layer_norm"""					)
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Dict          =	tax_attention_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""self_attention"""					)
			UpperCamelCase     :int          =	layer_norm
			UpperCamelCase     :Optional[Any]          =	k.T
			UpperCamelCase     :Tuple          =	o.T
			UpperCamelCase     :Optional[Any]          =	q.T
			UpperCamelCase     :int          =	v.T

			# Block i, layer 1 (Cross Attention).
			UpperCamelCase     :str          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""pre_cross_attention_layer_norm"""					)
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Tuple          =	tax_attention_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""encoder_decoder_attention"""					)
			UpperCamelCase     :str          =	layer_norm
			UpperCamelCase     :List[str]          =	k.T
			UpperCamelCase     :Optional[int]          =	o.T
			UpperCamelCase     :Union[str, Any]          =	q.T
			UpperCamelCase     :Tuple          =	v.T

			# Block i, layer 2 (MLP).
			UpperCamelCase     :Optional[Any]          =	tax_layer_norm_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			"""pre_mlp_layer_norm"""					)
			UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	tax_mlp_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""	,			__magic_name__					)
			UpperCamelCase     :str          =	layer_norm
			if split_mlp_wi:
				UpperCamelCase     :Any          =	wi[0].T
				UpperCamelCase     :int          =	wi[1].T
			else:
				UpperCamelCase     :List[str]          =	wi.T
			UpperCamelCase     :Union[str, Any]          =	wo.T

			if scalable_attention:
				# convert the rel_embedding of each layer
				UpperCamelCase     :Union[str, Any]          =	tax_relpos_bias_lookup(__magic_name__	,			__magic_name__	,			"""decoder"""					).T

		UpperCamelCase     :int          =	old["""decoder/decoder_norm/scale"""]

		# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
		if "decoder/logits_dense/kernel" in old:
			UpperCamelCase     :Optional[Any]          =	old["""decoder/logits_dense/kernel"""].T

	return new

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]	,			__magic_name__					:  bool					)		->			List[Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

	# Add what is missing.
	if "encoder.embed_tokens.weight" not in state_dict:
		UpperCamelCase     :int          =	state_dict["""shared.weight"""]

	if not is_encoder_only:
		if "decoder.embed_tokens.weight" not in state_dict:
			UpperCamelCase     :List[str]          =	state_dict["""shared.weight"""]

		if "lm_head.weight" not in state_dict:  # For old 1.0 models.
			print("""Using shared word embeddings as lm_head."""					)
			UpperCamelCase     :Optional[Any]          =	state_dict["""shared.weight"""]

	return state_dict

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  int	,			__magic_name__					:  List[str]					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :Any          =	checkpoints.load_tax_checkpoint(__magic_name__					)
	UpperCamelCase     :int          =	convert_tax_to_pytorch(
	    __magic_name__	,			num_layers=config.num_layers	,			is_encoder_only=__magic_name__	,			scalable_attention=__magic_name__					)
	UpperCamelCase     :Any          =	make_state_dict(__magic_name__	,			__magic_name__					)
	model.load_state_dict(__magic_name__	,			strict=__magic_name__					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  Dict	,			__magic_name__					:  str	,			__magic_name__					:  bool = False	,			__magic_name__					:  bool = False	,			)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	MTaConfig.from_json_file(__magic_name__					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	# Non-v1.1 checkpoints could also use T5Model, but this works for all.
	# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
	if is_encoder_only:
		UpperCamelCase     :List[str]          =	UMTaEncoderModel(__magic_name__					)
	else:
		UpperCamelCase     :List[Any]          =	UMTaForConditionalGeneration(__magic_name__					)

	# Load weights from tf checkpoint
	load_tax_weights_in_ta(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	model.save_pretrained(__magic_name__					)

	# Verify that we can load the checkpoint.
	model.from_pretrained(__magic_name__					)
	print("""Done"""					)


if __name__ == "__main__":
	UpperCAmelCase_ :						str      =      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
	# Required parameters
	parser.add_argument(
	    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	parser.add_argument(
	    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
	)
	parser.add_argument(
	    '''--scalable_attention''',
	    action='''store_true''',
	    help='''Whether the model uses scaled attention (umt5 model)''',
	    default=False,
	)
	UpperCAmelCase_ :						Tuple      =      parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path,
	    args.config_file,
	    args.pytorch_dump_path,
	    args.is_encoder_only,
	    args.scalable_attention,
	)




'''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'''
   )
   A__:    Dict					    =       None
A__:    Tuple					    =       {
    '''7B''': 1_1008,
    '''13B''': 1_3824,
    '''30B''': 1_7920,
    '''65B''': 2_2016,
    '''70B''': 2_8672,
}
A__:    Any					    =       {
    '''7B''': 1,
    '''7Bf''': 1,
    '''13B''': 2,
    '''13Bf''': 2,
    '''30B''': 4,
    '''65B''': 8,
    '''70B''': 8,
    '''70Bf''': 8,
}


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[int]    ,_UpperCAmelCase	:	Optional[int]=1    ,_UpperCAmelCase	:	List[str]=256  )  ->			Dict:
       return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3  )  ) + multiple_of - 1) // multiple_of)


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	List[Any]  )  ->			List[str]:
       with open(_UpperCAmelCase    ,"""r"""  ) as f:
              return json.load(_UpperCAmelCase  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Optional[Any]  )  ->			Tuple:
       with open(_UpperCAmelCase    ,"""w"""  ) as f:
              json.dump(_UpperCAmelCase    ,_UpperCAmelCase  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Optional[Any]    ,_UpperCAmelCase	:	int    ,_UpperCAmelCase	:	List[Any]=True  )  ->			Union[str, Any]:
       os.makedirs(_UpperCAmelCase    ,exist_ok=_UpperCAmelCase  )
       _a    : Union[str, Any]     					=os.path.join(_UpperCAmelCase    ,"""tmp"""  )
       os.makedirs(_UpperCAmelCase    ,exist_ok=_UpperCAmelCase  )

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

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

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

       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.)
              _a    : Any     					=torch.load(os.path.join(_UpperCAmelCase    ,"""consolidated.00.pth"""  )    ,map_location="""cpu"""  )
       else:
              # Sharded
              _a    : List[Any]     					=[
                  torch.load(os.path.join(_UpperCAmelCase    ,F"consolidated.{i:02d}.pth"  )    ,map_location="""cpu"""  )
                  for i in range(_UpperCAmelCase  )
              ]
       _a    : Any     					=0
       _a    : Optional[int]     					={"""weight_map""": {}}
       for layer_i in range(_UpperCAmelCase  ):
              _a    : List[str]     					=F"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
              if model_size == "7B":
                     # Unsharded
                     _a    : List[str]     					={
                         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.

                     _a    : Tuple     					={
                         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(),
                     }
                     _a    : str     					=permute(
                         torch.cat(
                             [
                                 loaded[i][F"layers.{layer_i}.attention.wq.weight"].view(_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase  )
                                 for i in range(_UpperCAmelCase  )
                             ]    ,dim=0    ,).reshape(_UpperCAmelCase    ,_UpperCAmelCase  )  )
                     _a    : Tuple     					=permute(
                         torch.cat(
                             [
                                 loaded[i][F"layers.{layer_i}.attention.wk.weight"].view(
                                     _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase  )
                                 for i in range(_UpperCAmelCase  )
                             ]    ,dim=0    ,).reshape(_UpperCAmelCase    ,_UpperCAmelCase  )    ,_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase    ,)
                     _a    : Any     					=torch.cat(
                         [
                             loaded[i][F"layers.{layer_i}.attention.wv.weight"].view(
                                 _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase  )
                             for i in range(_UpperCAmelCase  )
                         ]    ,dim=0    ,).reshape(_UpperCAmelCase    ,_UpperCAmelCase  )

                     _a    : List[str]     					=torch.cat(
                         [loaded[i][F"layers.{layer_i}.attention.wo.weight"] for i in range(_UpperCAmelCase  )]    ,dim=1  )
                     _a    : Union[str, Any]     					=torch.cat(
                         [loaded[i][F"layers.{layer_i}.feed_forward.w1.weight"] for i in range(_UpperCAmelCase  )]    ,dim=0  )
                     _a    : Tuple     					=torch.cat(
                         [loaded[i][F"layers.{layer_i}.feed_forward.w2.weight"] for i in range(_UpperCAmelCase  )]    ,dim=1  )
                     _a    : Union[str, Any]     					=torch.cat(
                         [loaded[i][F"layers.{layer_i}.feed_forward.w3.weight"] for i in range(_UpperCAmelCase  )]    ,dim=0  )

              _a    : str     					=inv_freq
              for k, v in state_dict.items():
                     _a    : Any     					=filename
                     param_count += v.numel()
              torch.save(_UpperCAmelCase    ,os.path.join(_UpperCAmelCase    ,_UpperCAmelCase  )  )

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

       for k, v in state_dict.items():
              _a    : Dict     					=filename
              param_count += v.numel()
       torch.save(_UpperCAmelCase    ,os.path.join(_UpperCAmelCase    ,_UpperCAmelCase  )  )

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

       # 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."""  )
       _a    : Any     					=LlamaForCausalLM.from_pretrained(_UpperCAmelCase    ,torch_dtype=torch.floataa    ,low_cpu_mem_usage=_UpperCAmelCase  )
       # Avoid saving this as part of the config.
       del model.config._name_or_path

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


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int    ,_UpperCAmelCase	:	int  )  ->			Optional[Any]:
       # Initialize the tokenizer based on the `spm` model
       _a    : List[str]     					=LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
       print(F"Saving a {tokenizer_class.__name__} to {tokenizer_path}."  )
       _a    : List[Any]     					=tokenizer_class(_UpperCAmelCase  )
       tokenizer.save_pretrained(_UpperCAmelCase  )


def 	SCREAMING_SNAKE_CASE_					(  )  ->			Union[str, Any]:
       _a    : List[str]     					=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=_UpperCAmelCase    ,help="""Whether or not to save using `safetensors`."""  )
       _a    : Optional[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    ,)
       _a    : List[Any]     					=os.path.join(args.input_dir    ,"""tokenizer.model"""  )
       write_tokenizer(args.output_dir    ,_UpperCAmelCase  )


if __name__ == "__main__":
   main()


