


from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def   _lowercase(					__a				:						NDArray[floataa]    ,     __a				:						NDArray[floataa]    ,     __a				:						list[int]    ,     __a				:						int    ,     ):
							a__    ,     a__      =coefficient_matrix.shape
							a__    ,     a__      =constant_matrix.shape

							if rowsa != colsa:
														a__      =f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
														raise ValueError(__a     )

							if colsa != 1:
														a__      =f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
														raise ValueError(__a     )

							if rowsa != rowsa:
														a__      =(
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
														)
														raise ValueError(__a     )

							if len(__a     ) != rowsa:
														a__      =(
														    'Number of initial values must be equal to number of rows in coefficient '
														    f"""matrix but received {len(__a     )} and {rowsa}"""
														)
														raise ValueError(__a     )

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'     )

							a__      =np.concatenate(
							    (coefficient_matrix, constant_matrix)    ,     axis=1     )

							a__    ,     a__      =table.shape

							strictly_diagonally_dominant(__a     )

							# Iterates the whole matrix for given number of times
							for _ in range(__a     ):
														a__      =[]
														for row in range(__a     ):
																					a__      =0
																					for col in range(__a     ):
																												if col == row:
																																			a__      =table[row][col]
																												elif col == cols - 1:
																																			a__      =table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					a__      =(temp + val) / denom
																					new_val.append(__a     )
														a__      =new_val

							return [float(__a     ) for i in new_val]






def   _lowercase(					__a				:						NDArray[floataa]     ):
							a__    ,     a__      =table.shape

							a__      =True

							for i in range(0    ,     __a     ):
														a__      =0
														for j in range(0    ,     cols - 1     ):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'     )

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
	import doctest

	doctest.testmod()









"""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


_lowerCAmelCase	:	Optional[int]				=      {
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

_lowerCAmelCase	:	Optional[Any]				=      {
    """169M""": 768,
    """430M""": 1_024,
    """1B5""": 2_048,
    """3B""": 2_560,
    """7B""": 4_096,
    """14B""": 5_120,
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any				)->		Tuple:






     '''simple docstring'''



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

          if name != "head.weight":
               UpperCAmelCase__						:    int							=							"rwkv." + name

          UpperCAmelCase__						:    Dict							=							weight
     return state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]			,					snake_case       : Optional[int]			,					snake_case       : Optional[Any]			,					snake_case       : Dict=None			,					snake_case       : List[Any]=None			,					snake_case       : List[str]=False			,					snake_case       : str=None				)->		int:






     '''simple docstring'''



     if tokenizer_file is None:
          print("No `--tokenizer_file` provided, we will use the default tokenizer."				)
          UpperCAmelCase__						:    str							=							5_0277
          UpperCAmelCase__						:    List[Any]							=							AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"				)
     else:
          UpperCAmelCase__						:    Union[str, Any]							=							PreTrainedTokenizerFast(tokenizer_file=snake_case				)
          UpperCAmelCase__						:    Tuple							=							len(snake_case				)
     tokenizer.save_pretrained(snake_case				)

     # 2. Build the config
     UpperCAmelCase__						:    Dict							=							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:
                    UpperCAmelCase__						:    List[Any]							=							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}.'				)

     UpperCAmelCase__						:    int							=							RwkvConfig(
         vocab_size=snake_case			,					num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,					hidden_size=HIDEN_SIZE_MAPPING[size]			,					)
     config.save_pretrained(snake_case				)

     # 3. Download model file then convert state_dict
     UpperCAmelCase__						:    Optional[int]							=							hf_hub_download(snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							torch.load(snake_case			,					map_location="cpu"				)
     UpperCAmelCase__						:    Tuple							=							convert_state_dict(snake_case				)

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

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

          # 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."				)
          UpperCAmelCase__						:    List[str]							=							list(shards.keys()				)

          del state_dict
          del shards
          gc.collect()

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

     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."				)
          UpperCAmelCase__						:    Optional[Any]							=							AutoModelForCausalLM.from_pretrained(snake_case				)
          model.push_to_hub(snake_case			,					max_shard_size="2GB"				)
          tokenizer.push_to_hub(snake_case				)


if __name__ == "__main__":
 _lowerCAmelCase	:	Optional[Any]				=      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.""",
 )

 _lowerCAmelCase	:	Dict				=      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,
 )


