




'''simple docstring'''


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_snake_case   :   Optional[int]     							=	TypeVar('T')
_snake_case   :   Union[str, Any]     							=	TypeVar('U')

class     A					(	Generic[T, U]					):







							def __init__(						self :						Tuple							,    lowerCAmelCase_ :						T | None							,    lowerCAmelCase_ :						U | None      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       key
														_a			  =       val
														_a			  =       None
														_a			  =       None







							def __repr__(						self :						Optional[Any]      )    ->							str:


														"""simple docstring"""


														return (
														    F'Node: key: {self.key}, val: {self.val}, '
														    F'has next: {bool(self.next      )}, has prev: {bool(self.prev      )}'
														)



class     A					(	Generic[T, U]					):







							def __init__(						self :						str      )    ->							None:


														"""simple docstring"""


														_a			  =       DoubleLinkedListNode(lowerCAmelCase_							,    lowerCAmelCase_      )
														_a			  =       DoubleLinkedListNode(lowerCAmelCase_							,    lowerCAmelCase_      )
														_a  ,							_a			  =       self.rear, self.head







							def __repr__(						self :						Dict      )    ->							str:


														"""simple docstring"""


														_a			  =       ['''DoubleLinkedList''']
														_a			  =       self.head
														while node.next is not None:
																					rep.append(str(lowerCAmelCase_      )      )
																					_a			  =       node.next
														rep.append(str(self.rear      )      )
														return ",\n    ".join(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						DoubleLinkedListNode[T, U]      )    ->							None:


														"""simple docstring"""


														_a			  =       self.rear.prev

														# All nodes other than self.head are guaranteed to have non-None previous
														assert previous is not None

														_a			  =       node
														_a			  =       previous
														_a			  =       node
														_a			  =       self.rear







							def      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						DoubleLinkedListNode[T, U]      )    ->							DoubleLinkedListNode[T, U] | None:


														"""simple docstring"""


														if node.prev is None or node.next is None:
																					return None

														_a			  =       node.next
														_a			  =       node.prev
														_a			  =       None
														_a			  =       None
														return node



class     A					(	Generic[T, U]					):
							lowercase_           =       {}







							def __init__(						self :						str							,    lowerCAmelCase_ :						int      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       DoubleLinkedList()
														_a			  =       capacity
														_a			  =       0
														_a			  =       0
														_a			  =       0
														_a			  =       {}







							def __repr__(						self :						int      )    ->							str:


														"""simple docstring"""


														return (
														    F'CacheInfo(hits={self.hits}, misses={self.miss}, '
														    F'capacity={self.capacity}, current size={self.num_keys})'
														)







							def __contains__(						self :						List[Any]							,    lowerCAmelCase_ :						T      )    ->							bool:


														"""simple docstring"""


														return key in self.cache







							def      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						T      )    ->							U | None:


														"""simple docstring"""


														if key in self.cache:
																					self.hits += 1
																					_a			  =       self.cache[key]
																					_a			  =       self.list.remove(self.cache[key]      )
																					assert node == value_node

																					# node is guaranteed not None because it is in self.cache
																					assert node is not None
																					self.list.add(lowerCAmelCase_      )
																					return node.val
														self.miss += 1
														return None







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						T							,    lowerCAmelCase_ :						U      )    ->							None:


														"""simple docstring"""


														if key not in self.cache:
																					if self.num_keys >= self.capacity:
																												# delete first node (oldest) when over capacity
																												_a			  =       self.list.head.next

																												# guaranteed to have a non-None first node when num_keys > 0
																												# explain to type checker via assertions
																												assert first_node is not None
																												assert first_node.key is not None
																												assert (
																												    self.list.remove(lowerCAmelCase_      ) is not None
																												)  # node guaranteed to be in list assert node.key is not None

																												del self.cache[first_node.key]
																												self.num_keys -= 1
																					_a			  =       DoubleLinkedListNode(lowerCAmelCase_							,    lowerCAmelCase_      )
																					self.list.add(self.cache[key]      )
																					self.num_keys += 1

														else:
																					# bump node to the end of the list, update value
																					_a			  =       self.list.remove(self.cache[key]      )
																					assert node is not None  # node guaranteed to be in list
																					_a			  =       value
																					self.list.add(lowerCAmelCase_      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						int							,    lowerCAmelCase_ :						int = 1_28      )    ->							Callable[[Callable[[T], U]], Callable[..., U]]:


														"""simple docstring"""


														def cache_decorator_inner(lowerCAmelCase_ :						Callable[[T], U]      ) -> Callable[..., U]:
																					def cache_decorator_wrapper(*lowerCAmelCase_ :						T      ) -> U:
																												if func not in cls.decorator_function_to_instance_map:
																																			_a			  =       LRUCache(lowerCAmelCase_      )

																												_a			  =       cls.decorator_function_to_instance_map[func].get(args[0]      )
																												if result is None:
																																			_a			  =       func(*lowerCAmelCase_      )
																																			cls.decorator_function_to_instance_map[func].put(args[0]							,    lowerCAmelCase_      )
																												return result

																					def cache_info() -> LRUCache[T, U]:
																												return cls.decorator_function_to_instance_map[func]

																					setattr(lowerCAmelCase_							,    '''cache_info'''							,    lowerCAmelCase_      )  # noqa: B010

																					return cache_decorator_wrapper

														return cache_decorator_inner


if __name__ == "__main__":
				import doctest

				doctest.testmod()







'''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'
				)
				_snake_case   :   int     							=	None
_snake_case   :   Tuple     							=	{
    '7B': 11008,
    '13B': 13824,
    '30B': 17920,
    '65B': 22016,
    '70B': 28672,
}
_snake_case   :   int     							=	{
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}
def 	snake_case_					(UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						Optional[Any]=1				,		UpperCamelCase :						Optional[Any]=256   ):





							'''simple docstring'''


							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3   )   ) + multiple_of - 1) // multiple_of)
def 	snake_case_					(UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							with open(UpperCamelCase				,		'''r'''   ) as f:
														return json.load(UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''simple docstring'''


							with open(UpperCamelCase				,		'''w'''   ) as f:
														json.dump(UpperCamelCase				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						List[str]				,		UpperCamelCase :						Union[str, Any]				,		UpperCamelCase :						str=True   ):





							'''simple docstring'''


							os.makedirs(UpperCamelCase				,		exist_ok=UpperCamelCase   )
							_a			  =       os.path.join(UpperCamelCase				,		'''tmp'''   )
							os.makedirs(UpperCamelCase				,		exist_ok=UpperCamelCase   )

							_a			  =       read_json(os.path.join(UpperCamelCase				,		'''params.json'''   )   )
							_a			  =       NUM_SHARDS[model_size]
							_a			  =       params['''n_layers''']
							_a			  =       params['''n_heads''']
							_a			  =       n_heads // num_shards
							_a			  =       params['''dim''']
							_a			  =       dim // n_heads
							_a			  =       10000.0
							_a			  =       1.0 / (base ** (torch.arange(0				,		UpperCamelCase				,		2   ).float() / dims_per_head))

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

							# permute for sliced rotary
							def permute(UpperCamelCase :						List[str]				,		UpperCamelCase :						str=n_heads				,		UpperCamelCase :						int=dim				,		UpperCamelCase :						Optional[Any]=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			  =       torch.load(os.path.join(UpperCamelCase				,		'''consolidated.00.pth'''   )				,		map_location='''cpu'''   )
							else:
														# Sharded
														_a			  =       [
														    torch.load(os.path.join(UpperCamelCase				,		f'consolidated.{i:02d}.pth'   )				,		map_location='''cpu'''   )
														    for i in range(UpperCamelCase   )
														]
							_a			  =       0
							_a			  =       {'''weight_map''': {}}
							for layer_i in range(UpperCamelCase   ):
														_a			  =       f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
														if model_size == "7B":
																					# Unsharded
																					_a			  =       {
																					    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			  =       {
																					    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			  =       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			  =       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			  =       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			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(UpperCamelCase   )]				,		dim=1   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(UpperCamelCase   )]				,		dim=0   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(UpperCamelCase   )]				,		dim=1   )
																					_a			  =       torch.cat(
																					    [loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(UpperCamelCase   )]				,		dim=0   )

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

							_a			  =       f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
							if model_size == "7B":
														# Unsharded
														_a			  =       {
														    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
														    '''model.norm.weight''': loaded['''norm.weight'''],
														    '''lm_head.weight''': loaded['''output.weight'''],
														}
							else:
														_a			  =       {
														    '''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			  =       filename
														param_count += v.numel()
							torch.save(UpperCamelCase				,		os.path.join(UpperCamelCase				,		UpperCamelCase   )   )

							# Write configs
							_a			  =       {'''total_size''': param_count * 2}
							write_json(UpperCamelCase				,		os.path.join(UpperCamelCase				,		'''pytorch_model.bin.index.json'''   )   )
							_a			  =       params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
							_a			  =       params['''multiple_of'''] if '''multiple_of''' in params else 256
							_a			  =       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			  =       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 	snake_case_					(UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


							_a			  =       LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.'   )
							_a			  =       tokenizer_class(UpperCamelCase   )
							tokenizer.save_pretrained(UpperCamelCase   )
def 	snake_case_					():





							'''simple docstring'''


							_a			  =       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			  =       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			  =       os.path.join(args.input_dir				,		'''tokenizer.model'''   )
							write_tokenizer(args.output_dir				,		UpperCamelCase   )


if __name__ == "__main__":
				main()

