

'''simple docstring'''






from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder






@dataclass
class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    "DiagonalGaussianDistribution"






class      lowercase_      (lowerCamelCase__							,			lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     True






  @register_to_config
  def __init__( self  :	Optional[int]   ,lowercase__  :	int = 3   ,lowercase__  :	int = 3   ,lowercase__  :	Tuple[str] = ("DownEncoderBlock2D",)   ,lowercase__  :	Tuple[str] = ("UpDecoderBlock2D",)   ,lowercase__  :	Tuple[int] = (6_4,)   ,lowercase__  :	int = 1   ,lowercase__  :	str = "silu"   ,lowercase__  :	int = 4   ,lowercase__  :	int = 3_2   ,lowercase__  :	int = 3_2   ,lowercase__  :	float = 0.1_8_2_1_5   ,):
   super().__init__()

   # pass init params to Encoder
   __lowercase				    =					Encoder(
       in_channels=lowercase__   ,out_channels=lowercase__   ,down_block_types=lowercase__   ,block_out_channels=lowercase__   ,layers_per_block=lowercase__   ,act_fn=lowercase__   ,norm_num_groups=lowercase__   ,double_z=lowercase__   ,)

   # pass init params to Decoder
   __lowercase				    =					Decoder(
       in_channels=lowercase__   ,out_channels=lowercase__   ,up_block_types=lowercase__   ,block_out_channels=lowercase__   ,layers_per_block=lowercase__   ,norm_num_groups=lowercase__   ,act_fn=lowercase__   ,)

   __lowercase				    =					nn.Convad(2 * latent_channels   ,2 * latent_channels   ,1     )
   __lowercase				    =					nn.Convad(lowercase__   ,lowercase__   ,1     )

   __lowercase				    =					False
   __lowercase				    =					False

   # only relevant if vae tiling is enabled
   __lowercase				    =					self.config.sample_size
   __lowercase				    =					(
       self.config.sample_size[0]
       if isinstance(self.config.sample_size   ,(list, tuple)     )
       else self.config.sample_size
   )
   __lowercase				    =					int(sample_size / (2 ** (len(self.config.block_out_channels     ) - 1))     )
   __lowercase				    =					0.2_5






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	str   ,lowercase__  :	str=False     ):
   if isinstance(lowercase__   ,(Encoder, Decoder)     ):
    __lowercase				    =					value






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	bool = True     ):
   __lowercase				    =					use_tiling






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   self.enable_tiling(lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					True






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					False






  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					{}

   def fn_recursive_add_processors(lowercase__  :	str   ,lowercase__  :	torch.nn.Module   ,lowercase__  :	Dict[str, AttentionProcessor]     ):
    if hasattr(lowercase__   ,'''set_processor'''     ):
     __lowercase				    =					module.processor

    for sub_name, child in module.named_children():
     fn_recursive_add_processors(F"{name}.{sub_name}"   ,lowercase__   ,lowercase__     )

    return processors

   for name, module in self.named_children():
    fn_recursive_add_processors(lowercase__   ,lowercase__   ,lowercase__     )

   return processors






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]   ,lowercase__  :	Union[AttentionProcessor, Dict[str, AttentionProcessor]]     ):
   __lowercase				    =					len(self.attn_processors.keys()     )

   if isinstance(lowercase__   ,lowercase__     ) and len(lowercase__     ) != count:
    raise ValueError(
        F"A dict of processors was passed, but the number of processors {len(lowercase__     )} does not match the"
        F" number of attention layers: {count}. Please make sure to pass {count} processor classes."     )

   def fn_recursive_attn_processor(lowercase__  :	str   ,lowercase__  :	torch.nn.Module   ,lowercase__  :	Tuple     ):
    if hasattr(lowercase__   ,'''set_processor'''     ):
     if not isinstance(lowercase__   ,lowercase__     ):
      module.set_processor(lowercase__     )
     else:
      module.set_processor(processor.pop(F"{name}.processor"     )     )

    for sub_name, child in module.named_children():
     fn_recursive_attn_processor(F"{name}.{sub_name}"   ,lowercase__   ,lowercase__     )

   for name, module in self.named_children():
    fn_recursive_attn_processor(lowercase__   ,lowercase__   ,lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   self.set_attn_processor(AttnProcessor()     )






  @apply_forward_hook
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	torch.FloatTensor   ,lowercase__  :	bool = True     ):
   if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
    return self.tiled_encode(lowercase__   ,return_dict=lowercase__     )

   if self.use_slicing and x.shape[0] > 1:
    __lowercase				    =					[self.encoder(lowercase__     ) for x_slice in x.split(1     )]
    __lowercase				    =					torch.cat(lowercase__     )
   else:
    __lowercase				    =					self.encoder(lowercase__     )

   __lowercase				    =					self.quant_conv(lowercase__     )
   __lowercase				    =					DiagonalGaussianDistribution(lowercase__     )

   if not return_dict:
    return (posterior,)

   return AutoencoderKLOutput(latent_dist=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	torch.FloatTensor   ,lowercase__  :	bool = True     ):
   if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
    return self.tiled_decode(lowercase__   ,return_dict=lowercase__     )

   __lowercase				    =					self.post_quant_conv(lowercase__     )
   __lowercase				    =					self.decoder(lowercase__     )

   if not return_dict:
    return (dec,)

   return DecoderOutput(sample=lowercase__     )






  @apply_forward_hook
  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	torch.FloatTensor   ,lowercase__  :	bool = True     ):
   if self.use_slicing and z.shape[0] > 1:
    __lowercase				    =					[self._decode(lowercase__     ).sample for z_slice in z.split(1     )]
    __lowercase				    =					torch.cat(lowercase__     )
   else:
    __lowercase				    =					self._decode(lowercase__     ).sample

   if not return_dict:
    return (decoded,)

   return DecoderOutput(sample=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	Tuple   ,lowercase__  :	Optional[int]   ,lowercase__  :	Tuple     ):
   __lowercase				    =					min(a.shape[2]   ,b.shape[2]   ,lowercase__     )
   for y in range(lowercase__     ):
    __lowercase				    =					a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
   return b






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]   ,lowercase__  :	Any   ,lowercase__  :	Union[str, Any]   ,lowercase__  :	Any     ):
   __lowercase				    =					min(a.shape[3]   ,b.shape[3]   ,lowercase__     )
   for x in range(lowercase__     ):
    __lowercase				    =					a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
   return b






  def    SCREAMING_SNAKE_CASE ( self  :	int   ,lowercase__  :	torch.FloatTensor   ,lowercase__  :	bool = True     ):
   __lowercase				    =					int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)     )
   __lowercase				    =					int(self.tile_latent_min_size * self.tile_overlap_factor     )
   __lowercase				    =					self.tile_latent_min_size - blend_extent

   # Split the image into 512x512 tiles and encode them separately.
   __lowercase				    =					[]
   for i in range(0   ,x.shape[2]   ,lowercase__     ):
    __lowercase				    =					[]
    for j in range(0   ,x.shape[3]   ,lowercase__     ):
     __lowercase				    =					x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
     __lowercase				    =					self.encoder(lowercase__     )
     __lowercase				    =					self.quant_conv(lowercase__     )
     row.append(lowercase__     )
    rows.append(lowercase__     )
   __lowercase				    =					[]
   for i, row in enumerate(lowercase__     ):
    __lowercase				    =					[]
    for j, tile in enumerate(lowercase__     ):
     # blend the above tile and the left tile
     # to the current tile and add the current tile to the result row
     if i > 0:
      __lowercase				    =					self.blend_v(rows[i - 1][j]   ,lowercase__   ,lowercase__     )
     if j > 0:
      __lowercase				    =					self.blend_h(row[j - 1]   ,lowercase__   ,lowercase__     )
     result_row.append(tile[:, :, :row_limit, :row_limit]     )
    result_rows.append(torch.cat(lowercase__   ,dim=3     )     )

   __lowercase				    =					torch.cat(lowercase__   ,dim=2     )
   __lowercase				    =					DiagonalGaussianDistribution(lowercase__     )

   if not return_dict:
    return (posterior,)

   return AutoencoderKLOutput(latent_dist=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	torch.FloatTensor   ,lowercase__  :	bool = True     ):
   __lowercase				    =					int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)     )
   __lowercase				    =					int(self.tile_sample_min_size * self.tile_overlap_factor     )
   __lowercase				    =					self.tile_sample_min_size - blend_extent

   # Split z into overlapping 64x64 tiles and decode them separately.
   # The tiles have an overlap to avoid seams between tiles.
   __lowercase				    =					[]
   for i in range(0   ,z.shape[2]   ,lowercase__     ):
    __lowercase				    =					[]
    for j in range(0   ,z.shape[3]   ,lowercase__     ):
     __lowercase				    =					z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
     __lowercase				    =					self.post_quant_conv(lowercase__     )
     __lowercase				    =					self.decoder(lowercase__     )
     row.append(lowercase__     )
    rows.append(lowercase__     )
   __lowercase				    =					[]
   for i, row in enumerate(lowercase__     ):
    __lowercase				    =					[]
    for j, tile in enumerate(lowercase__     ):
     # blend the above tile and the left tile
     # to the current tile and add the current tile to the result row
     if i > 0:
      __lowercase				    =					self.blend_v(rows[i - 1][j]   ,lowercase__   ,lowercase__     )
     if j > 0:
      __lowercase				    =					self.blend_h(row[j - 1]   ,lowercase__   ,lowercase__     )
     result_row.append(tile[:, :, :row_limit, :row_limit]     )
    result_rows.append(torch.cat(lowercase__   ,dim=3     )     )

   __lowercase				    =					torch.cat(lowercase__   ,dim=2     )
   if not return_dict:
    return (dec,)

   return DecoderOutput(sample=lowercase__     )






  def    SCREAMING_SNAKE_CASE ( self  :	Tuple   ,lowercase__  :	torch.FloatTensor   ,lowercase__  :	bool = False   ,lowercase__  :	bool = True   ,lowercase__  :	Optional[torch.Generator] = None   ,):
   __lowercase				    =					sample
   __lowercase				    =					self.encode(lowercase__     ).latent_dist
   if sample_posterior:
    __lowercase				    =					posterior.sample(generator=lowercase__     )
   else:
    __lowercase				    =					posterior.mode()
   __lowercase				    =					self.decode(lowercase__     ).sample

   if not return_dict:
    return (dec,)

   return DecoderOutput(sample=lowercase__     )







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()






