


"""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"""
 )
 __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 					lowercase      (    A_  ,       A_=1  ,       A_=256	)->				int:
  '''simple docstring'''







  return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3	)	) + multiple_of - 1) // multiple_of)


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  with open(__a  ,       "r"	) as f:
    return json.load(__a	)


def 					lowercase      (    A_  ,       A_	)->				int:
  '''simple docstring'''







  with open(__a  ,       "w"	) as f:
    json.dump(__a  ,       __a	)


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=True	)->				int:
  '''simple docstring'''







  os.makedirs(__a  ,       exist_ok=__a	)
  a						:		int											=      os.path.join(__a  ,       "tmp"	)
  os.makedirs(__a  ,       exist_ok=__a	)

  a						:		Union[str, Any]											=      read_json(os.path.join(__a  ,       "params.json"	)	)
  a						:		Any											=      NUM_SHARDS[model_size]
  a						:		str											=      params['n_layers']
  a						:		Dict											=      params['n_heads']
  a						:		Dict											=      n_heads // num_shards
  a						:		Dict											=      params['dim']
  a						:		Optional[Any]											=      dim // n_heads
  a						:		List[str]											=      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						:		Dict											=      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						:		int											=      n_heads
    a						:		List[str]											=      n_heads_per_shard
    a						:		Dict											=      dim

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

      a						:		List[Any]											=      {
          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						:		Optional[int]											=      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						:		Any											=      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						:		List[str]											=      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						:		List[str]											=      torch.cat(
          [loaded[i][F'''layers.{layer_i}.attention.wo.weight'''] for i in range(__a	)]  ,       dim=1	)
      a						:		Dict											=      torch.cat(
          [loaded[i][F'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(__a	)]  ,       dim=0	)
      a						:		str											=      torch.cat(
          [loaded[i][F'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(__a	)]  ,       dim=1	)
      a						:		Union[str, Any]											=      torch.cat(
          [loaded[i][F'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(__a	)]  ,       dim=0	)

    a						:		Optional[int]											=      inv_freq
    for k, v in state_dict.items():
      a						:		int											=      filename
      param_count += v.numel()
    torch.save(__a  ,       os.path.join(__a  ,       __a	)	)

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

  # Write configs
  a						:		Optional[int]											=      {'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						:		List[Any]											=      params['multiple_of'] if 'multiple_of' in params else 256
  a						:		Dict											=      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 					lowercase      (    A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a						:		Optional[Any]											=      LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
  print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''	)
  a						:		Tuple											=      tokenizer_class(__a	)
  tokenizer.save_pretrained(__a	)


def 					lowercase      (    )->				Union[str, Any]:
  '''simple docstring'''







  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						:		int											=      os.path.join(args.input_dir  ,       "tokenizer.model"	)
  write_tokenizer(args.output_dir  ,       __a	)


if __name__ == "__main__":
 main()



"""simple docstring"""







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_=5	)->				Union[str, Any]:
  '''simple docstring'''







  assert masked_input.count("<mask>"	) == 1
  a						:		List[str]											=      torch.tensor(tokenizer.encode(A_  ,       add_special_tokens=A_	)	).unsqueeze(0	)  # Batch size 1
  a						:		Dict											=      model(A_	)[0]  # The last hidden-state is the first element of the output tuple
  a						:		int											=      (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
  a						:		Optional[Any]											=      logits[0, masked_index, :]
  a						:		Dict											=      logits.softmax(dim=0	)
  a							,		a						:		Any											=      prob.topk(k=A_  ,       dim=0	)
  a						:		Optional[Any]											=      " ".join(
      [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A_	)	)]	)
  a						:		str											=      tokenizer.mask_token
  a						:		Any											=      []
  for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "	)	):
    a						:		Dict											=      predicted_token_bpe.replace("\u2581"  ,       " "	)
    if " {0}".format(A_	) in masked_input:
      topk_filled_outputs.append(
          (
              masked_input.replace(" {0}".format(A_	)  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
    else:
      topk_filled_outputs.append(
          (
              masked_input.replace(A_  ,       A_	),
              values[index].item(),
              predicted_token,
          )	)
  return topk_filled_outputs


__lowercase    =				CamembertTokenizer.from_pretrained("""camembert-base""")
__lowercase    =				CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

__lowercase    =				"""Le camembert est <mask> :)"""







print(fill_mask(masked_input, model, tokenizer, topk=3))






