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


def SCREAMING_SNAKE_CASE (   lowercase_:		NDArray[floataa]						,  lowercase_:		NDArray[floataa] ):
       _UpperCamelCase  ,					_UpperCamelCase   :				List[Any]       =     np.shape(lowercase_ )

       _UpperCamelCase   :				NDArray[floataa]       =     np.zeros((rows, 1)						,  dtype=lowercase_ )
       for row in reversed(range(lowercase_ ) ):
              _UpperCamelCase   :				str       =     0
              for col in range(row + 1						,  lowercase_ ):
                     total += coefficients[row, col] * x[col]

              _UpperCamelCase   :				Optional[int]       =     (vector[row] - total) / coefficients[row, row]

       return x


def SCREAMING_SNAKE_CASE (   lowercase_:		NDArray[floataa]						,  lowercase_:		NDArray[floataa] ):
       _UpperCamelCase  ,					_UpperCamelCase   :				int       =     np.shape(lowercase_ )
       if rows != columns:
              return np.array(()						,  dtype=lowercase_ )

       # augmented matrix
       _UpperCamelCase   :				NDArray[floataa]       =     np.concatenate((coefficients, vector)						,  axis=1 )
       _UpperCamelCase   :				str       =     augmented_mat.astype('float64' )

       # scale the matrix leaving it triangular
       for row in range(rows - 1 ):
              _UpperCamelCase   :				List[Any]       =     augmented_mat[row, row]
              for col in range(row + 1						,  lowercase_ ):
                     _UpperCamelCase   :				Optional[Any]       =     augmented_mat[col, row] / pivot
                     augmented_mat[col, :] -= factor * augmented_mat[row, :]

       _UpperCamelCase   :				Optional[Any]       =     retroactive_resolution(
           augmented_mat[:, 0:columns]						,  augmented_mat[:, columns : columns + 1] )

       return x


if __name__ == "__main__":
 import doctest

 doctest.testmod()




import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str ):
       if text.startswith(lowercase_ ):
              return text[len(lowercase_ ) :]
       return text  # or whatever


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     list(Path(lowercase_ ).glob('*.ckpt' ) )
              assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

       if len(lowercase_ ) > 1:
              logger.info(F"averaging the weights of {ckpt_files}" )

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     AutoTokenizer.from_pretrained(lowercase_ )
              tok.save_pretrained(lowercase_ )
       except Exception:
              pass
              # dont copy tokenizer if cant


if __name__ == "__main__":
 fire.Fire(convert_pl_to_hf)



