






"""simple docstring"""







def _a			(		_snake_case							):







      """simple docstring"""






      if upper_limit < 0:
            raise ValueError("""Limit for the Catalan sequence must be ≥ 0"""							)

      UpperCAmelCase          =	[0] * (upper_limit + 1)

      # Base case: C(0) = C(1) = 1
      UpperCAmelCase          =	1
      if upper_limit > 0:
            UpperCAmelCase          =	1

      # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
      for i in range(2				,	upper_limit + 1							):
            for j in range(_snake_case							):
                  catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

      return catalan_list


if __name__ == "__main__":
      print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
      print("""\n*** Enter -1 at any time to quit ***""")
      print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
      try:
            while True:
                  _UpperCamelCase					      =  int(input().strip())
                  if N < 0:
                        print("""\n********* Goodbye!! ************""")
                        break
                  else:
                        print(F"""The Catalan numbers from 0 through {N} are:""")
                        print(catalan_numbers(N))
                        print("""Try another upper limit for the sequence: """, end="""""")
      except (NameError, ValueError):
            print("""\n********* Invalid input, goodbye! ************\n""")

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

def _a			(		_snake_case				,	_snake_case=1				,	_snake_case=256							):







      """simple docstring"""






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

def _a			(		_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case				,	_snake_case							):







      """simple docstring"""






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

def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case=True							):







      """simple docstring"""






      os.makedirs(_snake_case				,	exist_ok=_snake_case							)
      UpperCAmelCase          =	os.path.join(_snake_case				,	"""tmp"""							)
      os.makedirs(_snake_case				,	exist_ok=_snake_case							)

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

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

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

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

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

      # Write configs
      UpperCAmelCase          =	{"""total_size""": param_count * 2}
      write_json(_snake_case				,	os.path.join(_snake_case				,	"""pytorch_model.bin.index.json"""							)							)
      UpperCAmelCase          =	params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
      UpperCAmelCase          =	params["""multiple_of"""] if """multiple_of""" in params else 256
      UpperCAmelCase          =	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          =	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				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
      print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''							)
      UpperCAmelCase          =	tokenizer_class(_snake_case							)
      tokenizer.save_pretrained(_snake_case							)

def _a			(		):







      """simple docstring"""






      UpperCAmelCase          =	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          =	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          =	os.path.join(args.input_dir				,	"""tokenizer.model"""							)
      write_tokenizer(args.output_dir				,	_snake_case							)


if __name__ == "__main__":
  main()



