





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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							Optional[Any]=1      ,    __lowerCAmelCase       :							Optional[int]=2_56		)		->  Optional[int]:
   return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3		)		) + multiple_of - 1) // multiple_of)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Any		)		->  Optional[Any]:
   with open(__lowerCAmelCase      ,    """r"""		) as f:
      return json.load(__lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str		)		->  Optional[Any]:
   with open(__lowerCAmelCase      ,    """w"""		) as f:
      json.dump(__lowerCAmelCase      ,    __lowerCAmelCase		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							List[Any]=True		)		->  Union[str, Any]:
   os.makedirs(__lowerCAmelCase      ,    exist_ok=__lowerCAmelCase		)
   snake_case 						=   os.path.join(__lowerCAmelCase      ,    """tmp"""		)
   os.makedirs(__lowerCAmelCase      ,    exist_ok=__lowerCAmelCase		)

   snake_case 						=   read_json(os.path.join(__lowerCAmelCase      ,    """params.json"""		)		)
   snake_case 						=   NUM_SHARDS[model_size]
   snake_case 						=   params["""n_layers"""]
   snake_case 						=   params["""n_heads"""]
   snake_case 						=   n_heads // num_shards
   snake_case 						=   params["""dim"""]
   snake_case 						=   dim // n_heads
   snake_case 						=   1_0000.0
   snake_case 						=   1.0 / (base ** (torch.arange(0      ,    __lowerCAmelCase      ,    2		).float() / dims_per_head))

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

   # permute for sliced rotary
   def permute(__lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Tuple=n_heads      ,    __lowerCAmelCase       :							str=dim      ,    __lowerCAmelCase       :							Dict=dim		):
      return w.view(__lowerCAmelCase      ,    dima // n_heads // 2      ,    2      ,    __lowerCAmelCase		).transpose(1      ,    2		).reshape(__lowerCAmelCase      ,    __lowerCAmelCase		)

   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.)
      snake_case 						=   torch.load(os.path.join(__lowerCAmelCase      ,    """consolidated.00.pth"""		)      ,    map_location="""cpu"""		)
   else:
      # Sharded
      snake_case 						=   [
          torch.load(os.path.join(__lowerCAmelCase      ,    F'''consolidated.{i:02d}.pth'''		)      ,    map_location="""cpu"""		)
          for i in range(__lowerCAmelCase		)
      ]
   snake_case 						=   0
   snake_case 						=   {"""weight_map""": {}}
   for layer_i in range(__lowerCAmelCase		):
      snake_case 						=   F'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
      if model_size == "7B":
         # Unsharded
         snake_case 						=   {
             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.

         snake_case 						=   {
             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(),
         }
         snake_case 						=   permute(
             torch.cat(
                 [
                     loaded[i][F'''layers.{layer_i}.attention.wq.weight'''].view(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
                     for i in range(__lowerCAmelCase		)
                 ]      ,    dim=0      ,    ).reshape(__lowerCAmelCase      ,    __lowerCAmelCase		)		)
         snake_case 						=   permute(
             torch.cat(
                 [
                     loaded[i][F'''layers.{layer_i}.attention.wk.weight'''].view(
                         __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
                     for i in range(__lowerCAmelCase		)
                 ]      ,    dim=0      ,    ).reshape(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    )
         snake_case 						=   torch.cat(
             [
                 loaded[i][F'''layers.{layer_i}.attention.wv.weight'''].view(
                     __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
                 for i in range(__lowerCAmelCase		)
             ]      ,    dim=0      ,    ).reshape(__lowerCAmelCase      ,    __lowerCAmelCase		)

         snake_case 						=   torch.cat(
             [loaded[i][F'''layers.{layer_i}.attention.wo.weight'''] for i in range(__lowerCAmelCase		)]      ,    dim=1		)
         snake_case 						=   torch.cat(
             [loaded[i][F'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(__lowerCAmelCase		)]      ,    dim=0		)
         snake_case 						=   torch.cat(
             [loaded[i][F'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(__lowerCAmelCase		)]      ,    dim=1		)
         snake_case 						=   torch.cat(
             [loaded[i][F'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(__lowerCAmelCase		)]      ,    dim=0		)

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

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

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

   # Write configs
   snake_case 						=   {"""total_size""": param_count * 2}
   write_json(__lowerCAmelCase      ,    os.path.join(__lowerCAmelCase      ,    """pytorch_model.bin.index.json"""		)		)
   snake_case 						=   params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
   snake_case 						=   params["""multiple_of"""] if """multiple_of""" in params else 2_56
   snake_case 						=   LlamaConfig(
       hidden_size=__lowerCAmelCase      ,    intermediate_size=compute_intermediate_size(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)      ,    num_attention_heads=params["""n_heads"""]      ,    num_hidden_layers=params["""n_layers"""]      ,    rms_norm_eps=params["""norm_eps"""]      ,    num_key_value_heads=__lowerCAmelCase      ,    )
   config.save_pretrained(__lowerCAmelCase		)

   # 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."""		)
   snake_case 						=   LlamaForCausalLM.from_pretrained(__lowerCAmelCase      ,    torch_dtype=torch.floataa      ,    low_cpu_mem_usage=__lowerCAmelCase		)
   # Avoid saving this as part of the config.
   del model.config._name_or_path

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





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							List[str]		)		->  List[str]:
   # Initialize the tokenizer based on the `spm` model
   snake_case 						=   LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
   print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''		)
   snake_case 						=   tokenizer_class(__lowerCAmelCase		)
   tokenizer.save_pretrained(__lowerCAmelCase		)





def 	__lowerCamelCase			(      )		->  str:
   snake_case 						=   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=__lowerCAmelCase      ,    help="""Whether or not to save using `safetensors`."""		)
   snake_case 						=   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      ,    )
   snake_case 						=   os.path.join(args.input_dir      ,    """tokenizer.model"""		)
   write_tokenizer(args.output_dir      ,    __lowerCAmelCase		)


if __name__ == "__main__":
 main()








'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "trajectory_transformer"
     snake_case_						      =   ["past_key_values"]
     snake_case_						      =   {
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }



     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			int=1_00   ,       __snake_case							:			List[str]=5   ,       __snake_case							:			str=1   ,       __snake_case							:			Union[str, Any]=1   ,       __snake_case							:			Union[str, Any]=2_49   ,       __snake_case							:			List[Any]=6   ,       __snake_case							:			Optional[int]=17   ,       __snake_case							:			Optional[Any]=25   ,       __snake_case							:			Union[str, Any]=4   ,       __snake_case							:			List[Any]=4   ,       __snake_case							:			Optional[int]=1_28   ,       __snake_case							:			Optional[Any]=0.1   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			int=0.1   ,       __snake_case							:			Union[str, Any]=0.00_06   ,       __snake_case							:			Tuple=5_12   ,       __snake_case							:			int=0.02   ,       __snake_case							:			Any=1e-12   ,       __snake_case							:			Optional[Any]=1   ,       __snake_case							:			List[Any]=True   ,       __snake_case							:			Dict=1   ,       __snake_case							:			Dict=5_02_56   ,       __snake_case							:			Union[str, Any]=5_02_56   ,       **__snake_case							:			Tuple   ,       )->	Optional[int]:
        snake_case 						=   vocab_size
        snake_case 						=   action_weight
        snake_case 						=   reward_weight
        snake_case 						=   value_weight
        snake_case 						=   max_position_embeddings
        snake_case 						=   block_size
        snake_case 						=   action_dim
        snake_case 						=   observation_dim
        snake_case 						=   transition_dim
        snake_case 						=   learning_rate
        snake_case 						=   n_layer
        snake_case 						=   n_head
        snake_case 						=   n_embd
        snake_case 						=   embd_pdrop
        snake_case 						=   attn_pdrop
        snake_case 						=   resid_pdrop
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   kaiming_initializer_range
        snake_case 						=   use_cache
        super().__init__(pad_token_id=__snake_case   ,       bos_token_id=__snake_case   ,       eos_token_id=__snake_case   ,       **__snake_case						)

