






from __future__ import annotations
def       a__						(  A		,				A					):
   lowercase__						:	Any         = '', '', ''
   lowercase__						:	Optional[Any]         = []

   for character in message_part:
      tmp.append(character_to_number[character]					)

   for each in tmp:
      one += each[0]
      two += each[1]
      three += each[2]

   return one + two + three
def       a__						(  A		,				A					):
   lowercase__						:	Tuple         = '', ''
   lowercase__						:	Optional[Any]         = []

   for character in message_part:
      this_part += character_to_number[character]

   for digit in this_part:
      tmp += digit
      if len(lowercase_					) == len(lowercase_					):
         result.append(lowercase_					)
         lowercase__						:	Dict         = ''

   return result[0], result[1], result[2]
def       a__						(  A		,				A					):
   # Validate message and alphabet, set to upper and remove spaces
   lowercase__						:	Optional[int]         = alphabet.replace(""" """		,				""""""					).upper()
   lowercase__						:	Any         = message.replace(""" """		,				""""""					).upper()

   # Check length and characters
   if len(lowercase_					) != 27:
      raise KeyError("""Length of alphabet has to be 27."""					)
   for each in message:
      if each not in alphabet:
         raise ValueError("""Each message character has to be included in alphabet!"""					)

    # Generate dictionares
   lowercase__						:	Any         = (
       '111',
       '112',
       '113',
       '121',
       '122',
       '123',
       '131',
       '132',
       '133',
       '211',
       '212',
       '213',
       '221',
       '222',
       '223',
       '231',
       '232',
       '233',
       '311',
       '312',
       '313',
       '321',
       '322',
       '323',
       '331',
       '332',
       '333',
   )
   lowercase__						:	Union[str, Any]         = {}
   lowercase__						:	Union[str, Any]         = {}
   for letter, number in zip(lowercase_		,				lowercase_					):
      lowercase__						:	List[str]         = number
      lowercase__						:	str         = letter

   return message, alphabet, character_to_number, number_to_character
def       a__						(  A		,				A = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,				A = 5					):
   lowercase__						:	int         = __prepare(
       lowercase_		,				lowercase_					)
   lowercase__						:	List[Any]         = '', ''

   for i in range(0		,				len(lowercase_					) + 1		,				lowercase_					):
      encrypted_numeric += __encrypt_part(
          message[i : i + period]		,				lowercase_					)

   for i in range(0		,				len(lowercase_					)		,				3					):
      encrypted += number_to_character[encrypted_numeric[i : i + 3]]

   return encrypted
def       a__						(  A		,				A = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,				A = 5					):
   lowercase__						:	Optional[int]         = __prepare(
       lowercase_		,				lowercase_					)
   lowercase__						:	Union[str, Any]         = []
   lowercase__						:	Optional[Any]         = ''

   for i in range(0		,				len(lowercase_					) + 1		,				lowercase_					):
      lowercase__						:	str         = __decrypt_part(message[i : i + period]		,				lowercase_					)

      for j in range(0		,				len(lowercase_					)					):
         decrypted_numeric.append(a[j] + b[j] + c[j]					)

   for each in decrypted_numeric:
      decrypted += number_to_character[each]

   return decrypted


if __name__ == "__main__":
 a    =       '''DEFEND THE EAST WALL OF THE CASTLE.'''
 a    =       encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
 a    =       decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
 print(F'Encrypted: {encrypted}\nDecrypted: {decrypted}')










import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
a    =       logging.get_logger(__name__)
def       a__						(  A		,				A		,				A					):
   lowercase__						:	int         = WavLMForSequenceClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	Dict         = downstream_dict["""projector.weight"""]
   lowercase__						:	int         = downstream_dict["""projector.bias"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.post_net.linear.weight"""]
   lowercase__						:	List[str]         = downstream_dict["""model.post_net.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Tuple         = WavLMForAudioFrameClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	List[Any]         = downstream_dict["""model.linear.weight"""]
   lowercase__						:	Optional[int]         = downstream_dict["""model.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Optional[int]         = WavLMForXVector.from_pretrained(A		,				config=A					)
   lowercase__						:	List[str]         = downstream_dict["""connector.weight"""]
   lowercase__						:	int         = downstream_dict["""connector.bias"""]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
      lowercase__						:	Optional[Any]         = downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      lowercase__						:	List[str]         = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   lowercase__						:	Union[str, Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
   lowercase__						:	Any         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
   lowercase__						:	List[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
   lowercase__						:	List[Any]         = downstream_dict["""objective.W"""]
   return model
@torch.no_grad()
def       a__						(  A		,				A		,				A		,				A					):
   lowercase__						:	List[Any]         = torch.load(A		,				map_location="""cpu"""					)

   lowercase__						:	Tuple         = checkpoint["""Downstream"""]

   lowercase__						:	List[str]         = WavLMConfig.from_pretrained(A					)
   lowercase__						:	Dict         = WavaVecaFeatureExtractor.from_pretrained(
       A		,				return_attention_mask=A		,				do_normalize=A					)

   lowercase__						:	Union[str, Any]         = hf_config.architectures[0]
   if arch.endswith("""ForSequenceClassification"""					):
      lowercase__						:	Union[str, Any]         = convert_classification(A		,				A		,				A					)
   elif arch.endswith("""ForAudioFrameClassification"""					):
      lowercase__						:	Dict         = convert_diarization(A		,				A		,				A					)
   elif arch.endswith("""ForXVector"""					):
      lowercase__						:	Dict         = convert_xvector(A		,				A		,				A					)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''					)

   if hf_config.use_weighted_layer_sum:
      lowercase__						:	Union[str, Any]         = checkpoint["""Featurizer"""]["""weights"""]

   hf_feature_extractor.save_pretrained(A					)
   hf_model.save_pretrained(A					)


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)


