

"""simple docstring"""





from __future__ import annotations

from typing import Any




def 						_A						(						*UpperCamelCase_  : list[list[int]])			->		list[list[int]]:
   '''simple docstring'''


   if all(_check_not_integer(UpperCamelCase_) for m in matrix_s):
      for i in matrix_s[1:]:
         _verify_matrix_sizes(matrix_s[0],    UpperCamelCase_)
      return [[sum(UpperCamelCase_) for t in zip(*UpperCamelCase_)] for m in zip(*UpperCamelCase_)]
   raise TypeError("Expected a matrix, got int/list instead")




def 						_A						(						UpperCamelCase_  : list[list[int]],    UpperCamelCase_  : list[list[int]])			->		list[list[int]]:
   '''simple docstring'''


   if (
       _check_not_integer(UpperCamelCase_)
       and _check_not_integer(UpperCamelCase_)
       and _verify_matrix_sizes(UpperCamelCase_,    UpperCamelCase_)
   ):
      return [[i - j for i, j in zip(*UpperCamelCase_)] for m in zip(UpperCamelCase_,    UpperCamelCase_)]
   raise TypeError("Expected a matrix, got int/list instead")




def 						_A						(						UpperCamelCase_  : list[list[int]],    UpperCamelCase_  : int | float)			->		list[list[float]]:
   '''simple docstring'''


   return [[x * n for x in row] for row in matrix]




def 						_A						(						UpperCamelCase_  : list[list[int]],    UpperCamelCase_  : list[list[int]])			->		list[list[int]]:
   '''simple docstring'''


   if _check_not_integer(UpperCamelCase_) and _check_not_integer(UpperCamelCase_):
      __lowercase	,__lowercase			=		_verify_matrix_sizes(UpperCamelCase_,    UpperCamelCase_)

   if cols[0] != rows[1]:
      __lowercase			=		(
          "Cannot multiply matrix of dimensions "
          F"""({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})"""
      )
      raise ValueError(UpperCamelCase_)
   return [
       [sum(m * n for m, n in zip(UpperCamelCase_,    UpperCamelCase_)) for j in zip(*UpperCamelCase_)] for i in matrix_a
   ]




def 						_A						(						UpperCamelCase_  : int)			->		list[list[int]]:
   '''simple docstring'''


   __lowercase			=		int(UpperCamelCase_)
   return [[int(row == column) for column in range(UpperCamelCase_)] for row in range(UpperCamelCase_)]




def 						_A						(						UpperCamelCase_  : list[list[int]],    UpperCamelCase_  : bool = True)			->		list[list[int]] | map[list[int]]:
   '''simple docstring'''


   if _check_not_integer(UpperCamelCase_):
      if return_map:
         return map(UpperCamelCase_,    zip(*UpperCamelCase_))
      else:
         return list(map(UpperCamelCase_,    zip(*UpperCamelCase_)))
   raise TypeError("Expected a matrix, got int/list instead")




def 						_A						(						UpperCamelCase_  : list[list[int]],    UpperCamelCase_  : int,    UpperCamelCase_  : int)			->		list[list[int]]:
   '''simple docstring'''


   __lowercase			=		matrix[:row] + matrix[row + 1 :]
   return [row[:column] + row[column + 1 :] for row in minor]




def 						_A						(						UpperCamelCase_  : list[list[int]])			->		Any:
   '''simple docstring'''


   if len(UpperCamelCase_) == 1:
      return matrix[0][0]

   return sum(
       x * determinant(minor(UpperCamelCase_,    0,    UpperCamelCase_)) * (-1) ** i
       for i, x in enumerate(matrix[0]))




def 						_A						(						UpperCamelCase_  : list[list[int]])			->		list[list[float]] | None:
   '''simple docstring'''


   __lowercase			=		determinant(UpperCamelCase_)
   if det == 0:
      return None

   __lowercase			=		[
       [determinant(minor(UpperCamelCase_,    UpperCamelCase_,    UpperCamelCase_)) for j in range(len(UpperCamelCase_))]
       for i in range(len(UpperCamelCase_))
   ]

   __lowercase			=		[
       [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row])]
       for row in range(len(UpperCamelCase_))
   ]
   __lowercase			=		list(transpose(UpperCamelCase_))
   return scalar_multiply(UpperCamelCase_,    1 / det)




def 						_A						(						UpperCamelCase_  : list[list[int]])			->		bool:
   '''simple docstring'''


   return not isinstance(UpperCamelCase_,    UpperCamelCase_) and not isinstance(matrix[0],    UpperCamelCase_)




def 						_A						(						UpperCamelCase_  : list[list[int]])			->		tuple[int, int]:
   '''simple docstring'''


   return len(UpperCamelCase_), len(matrix[0])




def 						_A						(						UpperCamelCase_  : list[list[int]],    UpperCamelCase_  : list[list[int]])			->		tuple[tuple[int, int], tuple[int, int]]:
   '''simple docstring'''


   __lowercase			=		_shape(UpperCamelCase_) + _shape(UpperCamelCase_)
   if shape[0] != shape[3] or shape[1] != shape[2]:
      __lowercase			=		(
          "operands could not be broadcast together with shape "
          F"""({shape[0], shape[1]}), ({shape[2], shape[3]})"""
      )
      raise ValueError(UpperCamelCase_)
   return (shape[0], shape[2]), (shape[1], shape[3])




def 						_A						(						)			->		None:
   '''simple docstring'''


   __lowercase			=		[[12, 10], [3, 9]]
   __lowercase			=		[[3, 4], [7, 4]]
   __lowercase			=		[[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
   __lowercase			=		[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
   print(F"""Add Operation, {add(UpperCamelCase_,    UpperCamelCase_) = } \n""")
   print(
       F"""Multiply Operation, {multiply(UpperCamelCase_,    UpperCamelCase_) = } \n""",    )
   print(F"""Identity: {identity(5)}\n""")
   print(F"""Minor of {matrix_c} = {minor(UpperCamelCase_,    1,    2)} \n""")
   print(F"""Determinant of {matrix_b} = {determinant(UpperCamelCase_)} \n""")
   print(F"""Inverse of {matrix_d} = {inverse(UpperCamelCase_)}\n""")


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()







"""simple docstring"""





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_a				   =							logging.get_logger(__name__)

_a				   =							['UtteranceLevel']




@torch.no_grad()
def 						_A						(						UpperCamelCase_  : Union[str, Any],    UpperCamelCase_  : int,    UpperCamelCase_  : Optional[int],    UpperCamelCase_  : Union[str, Any])			->		Any:
   '''simple docstring'''


   __lowercase			=		torch.load(UpperCamelCase_,    map_location="cpu")
   if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
      raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}""")

   __lowercase			=		checkpoint["Downstream"]

   __lowercase			=		HubertConfig.from_pretrained(UpperCamelCase_)
   __lowercase			=		HubertForSequenceClassification.from_pretrained(UpperCamelCase_,    config=UpperCamelCase_)
   __lowercase			=		WavaVecaFeatureExtractor.from_pretrained(
       UpperCamelCase_,    return_attention_mask=UpperCamelCase_,    do_normalize=UpperCamelCase_)

   if hf_congfig.use_weighted_layer_sum:
      __lowercase			=		checkpoint["Featurizer"]["weights"]

   __lowercase			=		downstream_dict["projector.weight"]
   __lowercase			=		downstream_dict["projector.bias"]
   __lowercase			=		downstream_dict["model.post_net.linear.weight"]
   __lowercase			=		downstream_dict["model.post_net.linear.bias"]

   hf_feature_extractor.save_pretrained(UpperCamelCase_)
   hf_model.save_pretrained(UpperCamelCase_)


if __name__ == "__main__":
   _a				   =							argparse.ArgumentParser()
   parser.add_argument(
       '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
   )
   parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
   parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
   _a				   =							parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




