



"""simple docstring"""







import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
							import bitsandbytes as bnb

from copy import deepcopy


lowerCAmelCase_   						=   logging.getLogger(__name__)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = None						,     __lowerCamelCase = False						,     )							->      int:
		lowercase__   :    Dict				=			bnb_quantization_config.load_in_abit
		lowercase__   :    int				=			bnb_quantization_config.load_in_abit

		if load_in_abit and not is_abit_bnb_available():
				raise ImportError(
				    '''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
				    ''' make sure you have the latest version of `bitsandbytes` installed.'''	)
		if load_in_abit and not is_abit_bnb_available():
				raise ValueError(
				    '''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
				    '''make sure you have the latest version of `bitsandbytes` installed.'''	)

		lowercase__   :    List[str]				=			[]
		# custom device map
		if isinstance(__lowerCamelCase						,     __lowerCamelCase	) and len(device_map.keys()	) > 1:
				lowercase__   :    str				=			[key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]

		# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
		if bnb_quantization_config.skip_modules is None:
				lowercase__   :    Any				=			get_keys_to_not_convert(__lowerCamelCase	)

		# add cpu modules to skip modules only for 4-bit modules
		if load_in_abit:
				bnb_quantization_config.skip_modules.extend(__lowerCamelCase	)
		lowercase__   :    Union[str, Any]				=			bnb_quantization_config.skip_modules

		# We add the modules we want to keep in full precision
		if bnb_quantization_config.keep_in_fpaa_modules is None:
				lowercase__   :    Union[str, Any]				=			[]
		lowercase__   :    Optional[Any]				=			bnb_quantization_config.keep_in_fpaa_modules
		modules_to_not_convert.extend(__lowerCamelCase	)

		# compatibility with peft
		lowercase__   :    Optional[Any]				=			load_in_abit
		lowercase__   :    Optional[Any]				=			load_in_abit

		lowercase__   :    str				=			get_parameter_device(__lowerCamelCase	)
		if model_device.type != "meta":
				# quantization of an already loaded model
				logger.warning(
				    '''It is not recommended to quantize a loaded model. '''
				    '''The model should be instantiated under the `init_empty_weights` context manager.'''	)
				lowercase__   :    Dict				=			replace_with_bnb_layers(__lowerCamelCase						,     __lowerCamelCase						,     modules_to_not_convert=__lowerCamelCase	)
				# convert param to the right dtype
				lowercase__   :    Dict				=			bnb_quantization_config.torch_dtype
				for name, param in model.state_dict().items():
						if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules	):
								param.to(torch.floataa	)
								if param.dtype != torch.floataa:
										lowercase__   :    Any				=			name.replace('''.weight'''						,     ''''''	).replace('''.bias'''						,     ''''''	)
										lowercase__   :    Dict				=			getattr(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
										if param is not None:
												param.to(torch.floataa	)
						elif torch.is_floating_point(__lowerCamelCase	):
								param.to(__lowerCamelCase	)
				if model_device.type == "cuda":
						# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
						model.cuda(torch.cuda.current_device()	)
						torch.cuda.empty_cache()
				elif torch.cuda.is_available():
						model.to(torch.cuda.current_device()	)
				else:
						raise RuntimeError('''No GPU found. A GPU is needed for quantization.'''	)
				logger.info(
				    f"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
				    '''We move the model to cuda.'''	)
				return model

		elif weights_location is None:
				raise RuntimeError(
				    f"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """	)

		else:
				with init_empty_weights():
						lowercase__   :    int				=			replace_with_bnb_layers(
						    __lowerCamelCase						,     __lowerCamelCase						,     modules_to_not_convert=__lowerCamelCase	)

				lowercase__   :    List[str]				=			get_quantized_model_device_map(
				    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     max_memory=__lowerCamelCase						,     no_split_module_classes=__lowerCamelCase						,     )
				if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
						lowercase__   :    List[str]				=			True

				lowercase__   :    int				=			any(x in list(device_map.values()	) for x in ['''cpu''', '''disk''']	)

				load_checkpoint_in_model(
				    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     dtype=bnb_quantization_config.torch_dtype						,     offload_folder=__lowerCamelCase						,     offload_state_dict=__lowerCamelCase						,     keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules						,     offload_abit_bnb=load_in_abit and offload						,     )
				return dispatch_model(__lowerCamelCase						,     device_map=__lowerCamelCase						,     offload_dir=__lowerCamelCase	)



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     __lowerCamelCase=None	)							->      int:
		if device_map is None:
				if torch.cuda.is_available():
						lowercase__   :    Optional[int]				=			{'''''': torch.cuda.current_device()}
				else:
						raise RuntimeError('''No GPU found. A GPU is needed for quantization.'''	)
				logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.'''	)

		if isinstance(__lowerCamelCase						,     __lowerCamelCase	):
				if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
						raise ValueError(
						    '''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
						    '''\'sequential\'.'''	)

				lowercase__   :    List[str]				=			{}
				special_dtypes.update(
				    {
				        name: bnb_quantization_config.torch_dtype
				        for name, _ in model.named_parameters()
				        if any(m in name for m in bnb_quantization_config.skip_modules	)
				    }	)
				special_dtypes.update(
				    {
				        name: torch.floataa
				        for name, _ in model.named_parameters()
				        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules	)
				    }	)

				lowercase__   :    List[str]				=			{}
				lowercase__   :    Tuple				=			special_dtypes
				lowercase__   :    Optional[int]				=			no_split_module_classes
				lowercase__   :    int				=			bnb_quantization_config.target_dtype

				# get max_memory for each device.
				if device_map != "sequential":
						lowercase__   :    Optional[Any]				=			get_balanced_memory(
						    __lowerCamelCase						,     low_zero=(device_map == '''balanced_low_0''')						,     max_memory=__lowerCamelCase						,     **__lowerCamelCase						,     )

				lowercase__   :    int				=			max_memory
				lowercase__   :    List[Any]				=			infer_auto_device_map(__lowerCamelCase						,     **__lowerCamelCase	)

		if isinstance(__lowerCamelCase						,     __lowerCamelCase	):
				# check if don't have any quantized module on the cpu
				lowercase__   :    Any				=			bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

				lowercase__   :    Union[str, Any]				=			{
				    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
				}
				for device in ["cpu", "disk"]:
						if device in device_map_without_some_modules.values():
								if bnb_quantization_config.load_in_abit:
										raise ValueError(
										    '''
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        '''	)
								else:
										logger.info(
										    '''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit'''	)
				del device_map_without_some_modules
		return device_map



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None	)							->      str:

		if modules_to_not_convert is None:
				lowercase__   :    Optional[int]				=			[]

		lowercase__		,			lowercase__   :    Optional[Any]				=			_replace_with_bnb_layers(
		    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
		if not has_been_replaced:
				logger.warning(
				    '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
				    ''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
				    ''' Please double check your model architecture, or submit an issue on github if you think this is'''
				    ''' a bug.'''	)
		return model



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase=None						,     __lowerCamelCase=None						,     )							->      int:
		lowercase__   :    Optional[Any]				=			False
		for name, module in model.named_children():
				if current_key_name is None:
						lowercase__   :    Optional[int]				=			[]
				current_key_name.append(__lowerCamelCase	)
				if isinstance(__lowerCamelCase						,     nn.Linear	) and name not in modules_to_not_convert:
						# Check if the current key is not in the `modules_to_not_convert`
						lowercase__   :    Union[str, Any]				=			'''.'''.join(__lowerCamelCase	)
						lowercase__   :    Dict				=			True
						for key in modules_to_not_convert:
								if (
								    (key in current_key_name_str) and (key + "." in current_key_name_str)
								) or key == current_key_name_str:
										lowercase__   :    int				=			False
										break
						if proceed:
								# Load bnb module with empty weight and replace ``nn.Linear` module
								if bnb_quantization_config.load_in_abit:
										lowercase__   :    Optional[int]				=			bnb.nn.LinearabitLt(
										    module.in_features						,     module.out_features						,     module.bias is not None						,     has_fpaa_weights=__lowerCamelCase						,     threshold=bnb_quantization_config.llm_inta_threshold						,     )
								elif bnb_quantization_config.load_in_abit:
										lowercase__   :    Dict				=			bnb.nn.Linearabit(
										    module.in_features						,     module.out_features						,     module.bias is not None						,     bnb_quantization_config.bnb_abit_compute_dtype						,     compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant						,     quant_type=bnb_quantization_config.bnb_abit_quant_type						,     )
								else:
										raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False'''	)
								lowercase__   :    List[str]				=			module.weight.data
								if module.bias is not None:
										lowercase__   :    Dict				=			module.bias.data
								bnb_module.requires_grad_(__lowerCamelCase	)
								setattr(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
								lowercase__   :    Tuple				=			True
				if len(list(module.children()	)	) > 0:
						lowercase__		,			lowercase__   :    int				=			_replace_with_bnb_layers(
						    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)
						lowercase__   :    Union[str, Any]				=			has_been_replaced | _has_been_replaced
				# Remove the last key for recursion
				current_key_name.pop(-1	)
		return model, has_been_replaced



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      List[str]:
		# Create a copy of the model
		with init_empty_weights():
				lowercase__   :    int				=			deepcopy(__lowerCamelCase	)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

		lowercase__   :    Optional[Any]				=			find_tied_parameters(__lowerCamelCase	)
		# For compatibility with Accelerate < 0.18
		if isinstance(__lowerCamelCase						,     __lowerCamelCase	):
				lowercase__   :    List[str]				=			sum(list(tied_params.values()	)						,     []	) + list(tied_params.keys()	)
		else:
				lowercase__   :    Optional[int]				=			sum(__lowerCamelCase						,     []	)
		lowercase__   :    List[str]				=			len(__lowerCamelCase	) > 0

		# Check if it is a base model
		lowercase__   :    Any				=			False
		if hasattr(__lowerCamelCase						,     '''base_model_prefix'''	):
				lowercase__   :    Optional[int]				=			not hasattr(__lowerCamelCase						,     model.base_model_prefix	)

		# Ignore this for base models (BertModel, GPT2Model, etc.)
		if (not has_tied_params) and is_base_model:
				return []

		# otherwise they have an attached head
		lowercase__   :    int				=			list(model.named_children()	)
		lowercase__   :    List[Any]				=			[list_modules[-1][0]]

		# add last module together with tied weights
		lowercase__   :    Union[str, Any]				=			set(__lowerCamelCase	) - set(__lowerCamelCase	)
		lowercase__   :    List[str]				=			list(set(__lowerCamelCase	)	) + list(__lowerCamelCase	)

		# remove ".weight" from the keys
		lowercase__   :    int				=			['''.weight''', '''.bias''']
		lowercase__   :    Optional[Any]				=			[]
		for name in list_untouched:
				for name_to_remove in names_to_remove:
						if name_to_remove in name:
								lowercase__   :    str				=			name.replace(__lowerCamelCase						,     ''''''	)
				filtered_module_names.append(__lowerCamelCase	)

		return filtered_module_names



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for m in model.modules():
				if isinstance(__lowerCamelCase						,     bnb.nn.Linearabit	):
						return True
		return False



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		return next(parameter.parameters()	).device





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      List[str]:
		# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
		if fpaa_statistics is None:
				set_module_tensor_to_device(__lowerCamelCase						,     __lowerCamelCase						,     0						,     dtype=__lowerCamelCase						,     value=__lowerCamelCase	)
				lowercase__   :    Any				=			param_name
				lowercase__   :    List[Any]				=			model
				if "." in tensor_name:
						lowercase__   :    Dict				=			tensor_name.split('''.'''	)
						for split in splits[:-1]:
								lowercase__   :    int				=			getattr(__lowerCamelCase						,     __lowerCamelCase	)
								if new_module is None:
										raise ValueError(f"""{module} has no attribute {split}."""	)
								lowercase__   :    int				=			new_module
						lowercase__   :    str				=			splits[-1]
				# offload weights
				lowercase__   :    Any				=			False
				offload_weight(module._parameters[tensor_name]						,     __lowerCamelCase						,     __lowerCamelCase						,     index=__lowerCamelCase	)
				if hasattr(module._parameters[tensor_name]						,     '''SCB'''	):
						offload_weight(
						    module._parameters[tensor_name].SCB						,     param_name.replace('''weight'''						,     '''SCB'''	)						,     __lowerCamelCase						,     index=__lowerCamelCase						,     )
		else:
				offload_weight(__lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     index=__lowerCamelCase	)
				offload_weight(__lowerCamelCase						,     param_name.replace('''weight'''						,     '''SCB'''	)						,     __lowerCamelCase						,     index=__lowerCamelCase	)

		set_module_tensor_to_device(__lowerCamelCase						,     __lowerCamelCase						,     '''meta'''						,     dtype=__lowerCamelCase						,     value=torch.empty(*param.size()	)	)




'''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()


