
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'
      )
      lowercase__         =None
lowercase__         ={
    '''7B''': 11008,
    '''13B''': 13824,
    '''30B''': 17920,
    '''65B''': 22016,
    '''70B''': 28672,
}
lowercase__         ={
    '''7B''': 1,
    '''7Bf''': 1,
    '''13B''': 2,
    '''13Bf''': 2,
    '''30B''': 4,
    '''65B''': 8,
    '''70B''': 8,
    '''70Bf''': 8,
}




def   __UpperCamelCase	(							lowerCAmelCase__				:							Dict    ,  lowerCAmelCase__				:							Dict=1    ,  lowerCAmelCase__				:							Optional[Any]=2_5_6    ):
 return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3    )    ) + multiple_of - 1) // multiple_of)




def   __UpperCamelCase	(							lowerCAmelCase__				:							Optional[Any]    ):
 with open(A_    ,  '''r'''    ) as f:
  return json.load(A_    )




def   __UpperCamelCase	(							lowerCAmelCase__				:							Dict    ,  lowerCAmelCase__				:							int    ):
 with open(A_    ,  '''w'''    ) as f:
  json.dump(A_    ,  A_    )




def   __UpperCamelCase	(							lowerCAmelCase__				:							Optional[Any]    ,  lowerCAmelCase__				:							int    ,  lowerCAmelCase__				:							Tuple    ,  lowerCAmelCase__				:							int=True    ):
 os.makedirs(A_    ,  exist_ok=A_    )
 __a					:    Tuple													=    os.path.join(A_    ,  '''tmp'''    )
 os.makedirs(A_    ,  exist_ok=A_    )

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

 if "n_kv_heads" in params:
  __a					:    Tuple													=    params['''n_kv_heads''']  # for GQA / MQA
  __a					:    Union[str, Any]													=    n_heads_per_shard // num_key_value_heads
  __a					:    str													=    dim // num_key_value_heads
 else:  # compatibility with other checkpoints
  __a					:    Tuple													=    n_heads
  __a					:    Union[str, Any]													=    n_heads_per_shard
  __a					:    Dict													=    dim

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

 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					:    str													=    torch.load(os.path.join(A_    ,  '''consolidated.00.pth'''    )    ,  map_location='''cpu'''    )
 else:
  # Sharded
  __a					:    List[str]													=    [
      torch.load(os.path.join(A_    ,  f"consolidated.{i:02d}.pth"    )    ,  map_location='''cpu'''    )
      for i in range(A_    )
  ]
 __a					:    List[Any]													=    0
 __a					:    str													=    {'''weight_map''': {}}
 for layer_i in range(A_    ):
  __a					:    Tuple													=    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					:    List[str]													=    {
       f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
           f"layers.{layer_i}.attention_norm.weight"
       ].clone(),
       f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
           f"layers.{layer_i}.ffn_norm.weight"
       ].clone(),
   }
   __a					:    Union[str, Any]													=    permute(
       torch.cat(
           [
               loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(A_    ,  A_    ,  A_    )
               for i in range(A_    )
           ]    ,  dim=0    ,  ).reshape(A_    ,  A_    )    )
   __a					:    List[str]													=    permute(
       torch.cat(
           [
               loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
                   A_    ,  A_    ,  A_    )
               for i in range(A_    )
           ]    ,  dim=0    ,  ).reshape(A_    ,  A_    )    ,  A_    ,  A_    ,  A_    ,  )
   __a					:    Optional[int]													=    torch.cat(
       [
           loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(
               A_    ,  A_    ,  A_    )
           for i in range(A_    )
       ]    ,  dim=0    ,  ).reshape(A_    ,  A_    )

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

  __a					:    Dict													=    inv_freq
  for k, v in state_dict.items():
   __a					:    List[str]													=    filename
   param_count += v.numel()
  torch.save(A_    ,  os.path.join(A_    ,  A_    )    )

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

 for k, v in state_dict.items():
  __a					:    str													=    filename
  param_count += v.numel()
 torch.save(A_    ,  os.path.join(A_    ,  A_    )    )

 # Write configs
 __a					:    Union[str, Any]													=    {'''total_size''': param_count * 2}
 write_json(A_    ,  os.path.join(A_    ,  '''pytorch_model.bin.index.json'''    )    )
 __a					:    Union[str, Any]													=    params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
 __a					:    Any													=    params['''multiple_of'''] if '''multiple_of''' in params else 2_5_6
 __a					:    List[str]													=    LlamaConfig(
     hidden_size=A_    ,  intermediate_size=compute_intermediate_size(A_    ,  A_    ,  A_    )    ,  num_attention_heads=params['''n_heads''']    ,  num_hidden_layers=params['''n_layers''']    ,  rms_norm_eps=params['''norm_eps''']    ,  num_key_value_heads=A_    ,  )
 config.save_pretrained(A_    )

 # 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					:    Optional[int]													=    LlamaForCausalLM.from_pretrained(A_    ,  torch_dtype=torch.floataa    ,  low_cpu_mem_usage=A_    )
 # Avoid saving this as part of the config.
 del model.config._name_or_path

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




def   __UpperCamelCase	(							lowerCAmelCase__				:							Union[str, Any]    ,  lowerCAmelCase__				:							List[str]    ):
 # 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					:    Union[str, Any]													=    tokenizer_class(A_    )
 tokenizer.save_pretrained(A_    )




def   __UpperCamelCase	(							):
 __a					:    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=A_    ,  help='''Whether or not to save using `safetensors`.'''    )
 __a					:    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    ,  )
 __a					:    Any													=    os.path.join(args.input_dir    ,  '''tokenizer.model'''    )
 write_tokenizer(args.output_dir    ,  A_    )


if __name__ == "__main__":
      main()



"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_					):
	for i in range(len(A_					) - 1		,	0		,	-1					):
		lowerCAmelCase__      :		Optional[Any]    							=      False

		for j in range(A_		,	0		,	-1					):
			if unsorted[j] < unsorted[j - 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j - 1], unsorted[j]
				lowerCAmelCase__      :		Dict    							=      True

		for j in range(A_					):
			if unsorted[j] > unsorted[j + 1]:
				lowerCAmelCase__							,lowerCAmelCase__      :		Union[str, Any]    							=      unsorted[j + 1], unsorted[j]
				lowerCAmelCase__      :		Any    							=      True

		if not swapped:
			break
	return unsorted


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							__UpperCamelCase    : int				 =						input('''Enter numbers separated by a comma:\n''').strip()
							__UpperCamelCase    : Optional[Any]				 =						[int(item) for item in user_input.split(''',''')]
							print(F'''{cocktail_shaker_sort(unsorted) = }''')




