






"""simple docstring"""




import unittest

import numpy as np

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__ = None     ,   ):



  '''simple docstring'''
  _a  :       List[Any]             =	np.shape(UpperCamelCase__				)
  _a  :       Any             =	np.shape(UpperCamelCase__				)
  _a  :       Union[str, Any]             =	np.shape(UpperCamelCase__				)

  if shape_a[0] != shape_b[0]:
    _a  :       int             =	(
        """Expected the same number of rows for A and B. """
        F"""Instead found A of size {shape_a} and B of size {shape_b}"""
    )
    raise ValueError(UpperCamelCase__				)

  if shape_b[1] != shape_c[1]:
    _a  :       Tuple             =	(
        """Expected the same number of columns for B and C. """
        F"""Instead found B of size {shape_b} and C of size {shape_c}"""
    )
    raise ValueError(UpperCamelCase__				)

  _a  :       int             =	pseudo_inv
  if a_inv is None:
    try:
      _a  :       Optional[int]             =	np.linalg.inv(UpperCamelCase__				)
    except np.linalg.LinAlgError:
      raise ValueError(
          """Input matrix A is not invertible. Cannot compute Schur complement."""				)

  return mat_c - mat_b.T @ a_inv @ mat_b






class        UpperCamelCase			(	unittest.TestCase      ):


 def _lowercase	(     self		:						int							)					->       None:
   _a  :       str             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Tuple             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       Optional[int]             =	np.array([[2, 1], [6, 3]]							)

   _a  :       Optional[Any]             =	schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	np.block([[a, b], [b.T, c]]							)

   _a  :       int             =	np.linalg.det(UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	np.linalg.det(UpperCAmelCase__							)
   _a  :       List[Any]             =	np.linalg.det(UpperCAmelCase__							)

   self.assertAlmostEqual(UpperCAmelCase__							,						det_a * det_s							)


 def _lowercase	(     self		:						int							)					->       None:
   _a  :       Optional[int]             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Optional[int]             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       Union[str, Any]             =	np.array([[2, 1], [6, 3]]							)

   with self.assertRaises(UpperCAmelCase__							):
     schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)







 def _lowercase	(     self		:						List[Any]							)					->       None:
   _a  :       Any             =	np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
   _a  :       Dict             =	np.array([[0, 3], [3, 0], [2, 3]]							)
   _a  :       List[Any]             =	np.array([[2, 1, 3], [6, 3, 5]]							)

   with self.assertRaises(UpperCAmelCase__							):
     schur_complement(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 unittest.main()









"""simple docstring"""




import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_snake_case          =   {
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

_snake_case          =   {
    '169M': 768,
    '430M': 1024,
    '1B5': 2048,
    '3B': 2560,
    '7B': 4096,
    '14B': 5120,
}

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       int             =	list(state_dict.keys()				)
  for name in state_dict_keys:
    _a  :       str             =	state_dict.pop(UpperCamelCase__				)
    # emb -> embedding
    if name.startswith("""emb."""				):
      _a  :       Dict             =	name.replace("""emb."""     ,   """embeddings."""				)
    # ln_0 -> pre_ln (only present at block 0)
    if name.startswith("""blocks.0.ln0"""				):
      _a  :       Dict             =	name.replace("""blocks.0.ln0"""     ,   """blocks.0.pre_ln"""				)
    # att -> attention
    _a  :       Any             =	re.sub(R"""blocks\.(\d+)\.att"""     ,   R"""blocks.\1.attention"""     ,   UpperCamelCase__				)
    # ffn -> feed_forward
    _a  :       int             =	re.sub(R"""blocks\.(\d+)\.ffn"""     ,   R"""blocks.\1.feed_forward"""     ,   UpperCamelCase__				)
    # time_mix_k -> time_mix_key and reshape
    if name.endswith(""".time_mix_k"""				):
      _a  :       List[str]             =	name.replace(""".time_mix_k"""     ,   """.time_mix_key"""				)
    # time_mix_v -> time_mix_value and reshape
    if name.endswith(""".time_mix_v"""				):
      _a  :       Tuple             =	name.replace(""".time_mix_v"""     ,   """.time_mix_value"""				)
    # time_mix_r -> time_mix_key and reshape
    if name.endswith(""".time_mix_r"""				):
      _a  :       Dict             =	name.replace(""".time_mix_r"""     ,   """.time_mix_receptance"""				)

    if name != "head.weight":
      _a  :       Optional[int]             =	"""rwkv.""" + name

    _a  :       Any             =	weight
  return state_dict

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None     ,   UpperCamelCase__=None     ,   UpperCamelCase__=False     ,   UpperCamelCase__=None				):



  '''simple docstring'''
  # 1. If possible, build the tokenizer.
  if tokenizer_file is None:
    print("""No `--tokenizer_file` provided, we will use the default tokenizer."""				)
    _a  :       Tuple             =	5_0_2_7_7
    _a  :       str             =	AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b"""				)
  else:
    _a  :       int             =	PreTrainedTokenizerFast(tokenizer_file=UpperCamelCase__				)
    _a  :       int             =	len(UpperCamelCase__				)
  tokenizer.save_pretrained(UpperCamelCase__				)

  # 2. Build the config
  _a  :       Optional[Any]             =	list(NUM_HIDDEN_LAYERS_MAPPING.keys()				)
  if size is None:
    # Try to infer size from the checkpoint name
    for candidate in possible_sizes:
      if candidate in checkpoint_file:
        _a  :       Tuple             =	candidate
        break
    if size is None:
      raise ValueError("""Could not infer the size, please provide it with the `--size` argument."""				)
  if size not in possible_sizes:
    raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}."""				)

  _a  :       List[Any]             =	RwkvConfig(
      vocab_size=UpperCamelCase__     ,   num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]     ,   hidden_size=HIDEN_SIZE_MAPPING[size]     ,   )
  config.save_pretrained(UpperCamelCase__				)

  # 3. Download model file then convert state_dict
  _a  :       str             =	hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__				)
  _a  :       int             =	torch.load(UpperCamelCase__     ,   map_location="""cpu"""				)
  _a  :       List[str]             =	convert_state_dict(UpperCamelCase__				)

  # 4. Split in shards and save
  _a      ,  _a  :       List[str]             =	shard_checkpoint(UpperCamelCase__				)
  for shard_file, shard in shards.items():
    torch.save(UpperCamelCase__     ,   os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)

  if index is not None:
    _a  :       Optional[Any]             =	os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)
    # Save the index as well
    with open(UpperCamelCase__     ,   """w"""     ,   encoding="""utf-8"""				) as f:
      _a  :       Dict             =	json.dumps(UpperCamelCase__     ,   indent=2     ,   sort_keys=UpperCamelCase__				) + """\n"""
      f.write(UpperCamelCase__				)

    # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
    print(
        """Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."""				)
    _a  :       List[Any]             =	list(shards.keys()				)

    del state_dict
    del shards
    gc.collect()

    for shard_file in shard_files:
      _a  :       Any             =	torch.load(os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)
      torch.save({k: v.cpu().clone() for k, v in state_dict.items()}     ,   os.path.join(UpperCamelCase__     ,   UpperCamelCase__				)				)

  del state_dict
  gc.collect()

  if push_to_hub:
    if model_name is None:
      raise ValueError("""Please provide a `model_name` to push the model to the Hub."""				)
    _a  :       Dict             =	AutoModelForCausalLM.from_pretrained(UpperCamelCase__				)
    model.push_to_hub(UpperCamelCase__     ,   max_shard_size="""2GB"""				)
    tokenizer.push_to_hub(UpperCamelCase__				)


if __name__ == "__main__":
 _snake_case          =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--repo_id', default=None, type=str, required=True, help='Repo ID from which to pull the checkpoint.'
 )
 parser.add_argument(
     '--checkpoint_file', default=None, type=str, required=True, help='Name of the checkpoint file in the repo.'
 )
 parser.add_argument(
     '--output_dir', default=None, type=str, required=True, help='Where to save the converted model.'
 )
 parser.add_argument(
     '--tokenizer_file',
     default=None,
     type=str,
     help='Path to the tokenizer file to use (if not provided, only the model is converted).',
 )
 parser.add_argument(
     '--size',
     default=None,
     type=str,
     help='Size of the model. Will be inferred from the `checkpoint_file` if not passed.',
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
     help='Push to the Hub the converted model.',
 )
 parser.add_argument(
     '--model_name',
     default=None,
     type=str,
     help='Name of the pushed model on the Hub, including the username / organization.',
 )

 _snake_case          =   parser.parse_args()
 convert_rmkv_checkpoint_to_hf_format(
     args.repo_id,
     args.checkpoint_file,
     args.output_dir,
     size=args.size,
     tokenizer_file=args.tokenizer_file,
     push_to_hub=args.push_to_hub,
     model_name=args.model_name,
 )

