



'''simple docstring'''





from __future__ import annotations
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   snake_case  ,    snake_case  ,    snake_case   :							Optional[Any]						  ='''''', '''''', ''''''
   snake_case   :							List[str]						  =[]

   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   (  lowerCamelCase_					,						lowerCamelCase_							):
   snake_case  ,    snake_case   :							Optional[int]						  ='''''', ''''''
   snake_case   :							Tuple						  =[]

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

   for digit in this_part:
      tmp += digit
      if len(lowerCamelCase_							) == len(lowerCamelCase_							):
         result.append(lowerCamelCase_							)
         snake_case   :							str						  =''''''

   return result[0], result[1], result[2]
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   # Validate message and alphabet, set to upper and remove spaces
   snake_case   :							Union[str, Any]						  =alphabet.replace(''' '''					,						''''''							).upper()
   snake_case   :							Tuple						  =message.replace(''' '''					,						''''''							).upper()

   # Check length and characters
   if len(lowerCamelCase_							) != 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
   snake_case   :							List[str]						  =(
       '''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''',
   )
   snake_case   :							Optional[Any]						  ={}
   snake_case   :							Dict						  ={}
   for letter, number in zip(lowerCamelCase_					,						lowerCamelCase_							):
      snake_case   :							Union[str, Any]						  =number
      snake_case   :							Dict						  =letter

   return message, alphabet, character_to_number, number_to_character
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."					,						lowerCamelCase_ = 5							):
   snake_case  ,    snake_case  ,    snake_case  ,    snake_case   :							Any						  =__prepare(
       lowerCamelCase_					,						lowerCamelCase_							)
   snake_case  ,    snake_case   :							Optional[Any]						  ='''''', ''''''

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

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

   return encrypted
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."					,						lowerCamelCase_ = 5							):
   snake_case  ,    snake_case  ,    snake_case  ,    snake_case   :							Tuple						  =__prepare(
       lowerCamelCase_					,						lowerCamelCase_							)
   snake_case   :							List[Any]						  =[]
   snake_case   :							int						  =''''''

   for i in range(0					,						len(lowerCamelCase_							) + 1					,						lowerCamelCase_							):
      snake_case  ,    snake_case  ,    snake_case   :							List[Any]						  =__decrypt_part(message[i : i + period]					,						lowerCamelCase_							)

      for j in range(0					,						len(lowerCamelCase_							)							):
         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			:   Optional[Any]			=      """DEFEND THE EAST WALL OF THE CASTLE."""
      A			:   Any			=      encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
      A			:   Optional[int]			=      decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
      print(f"Encrypted: {encrypted}\nDecrypted: {decrypted}")










'''simple docstring'''





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A			:   Dict			=      logging.get_logger(__name__)

A			:   Tuple			=      {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """mask_emb""": """masked_spec_embed""",
}
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   for attribute in key.split('''.'''							):
      snake_case   :							Optional[Any]						  =getattr(lowerCamelCase_					,						lowerCamelCase_							)

   if weight_type is not None:
      snake_case   :							int						  =getattr(lowerCamelCase_					,						lowerCamelCase_							).shape
   else:
      snake_case   :							Any						  =hf_pointer.shape

   assert hf_shape == value.shape, (
       F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
       F''' {value.shape} for {full_name}'''
   )

   if weight_type == "weight":
      snake_case   :							Dict						  =value
   elif weight_type == "weight_g":
      snake_case   :							Any						  =value
   elif weight_type == "weight_v":
      snake_case   :							int						  =value
   elif weight_type == "bias":
      snake_case   :							int						  =value
   else:
      snake_case   :							str						  =value

   logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							str						  =[]
   snake_case   :							Tuple						  =fairseq_model.state_dict()

   snake_case   :							Union[str, Any]						  =hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      snake_case   :							Optional[int]						  =False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						hf_model.config.feat_extract_norm == '''group'''					,						)
         snake_case   :							int						  =True
      else:
         for key, mapped_key in MAPPING.items():
            snake_case   :							Dict						  =mapped_key

            if key in name:
               snake_case   :							str						  =True
               if "*" in mapped_key:
                  snake_case   :							Tuple						  =name.split(lowerCamelCase_							)[0].split('''.'''							)[-2]
                  snake_case   :							Optional[int]						  =mapped_key.replace('''*'''					,						lowerCamelCase_							)
               if "weight_g" in name:
                  snake_case   :							int						  ='''weight_g'''
               elif "weight_v" in name:
                  snake_case   :							List[str]						  ='''weight_v'''
               elif "weight" in name:
                  snake_case   :							Any						  ='''weight'''
               elif "bias" in name:
                  snake_case   :							List[str]						  ='''bias'''
               else:
                  snake_case   :							Tuple						  =None
               set_recursively(lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							)
            continue
      if not is_used:
         unused_weights.append(lowerCamelCase_							)

   logger.warning(F'''Unused weights: {unused_weights}'''							)
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
   snake_case   :							List[str]						  =full_name.split('''conv_layers.'''							)[-1]
   snake_case   :							Any						  =name.split('''.'''							)
   snake_case   :							Tuple						  =int(items[0]							)
   snake_case   :							int						  =int(items[1]							)

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
         )
         snake_case   :							Optional[int]						  =value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
         )
         snake_case   :							str						  =value
         logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
             " found."
         )
         snake_case   :							Optional[int]						  =value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F'''{full_name} has size {value.shape}, but'''
             F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
         )
         snake_case   :							Union[str, Any]						  =value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
   else:
      unused_weights.append(lowerCamelCase_							)
def 		_a   (  lowerCamelCase_							):
   snake_case   :							Any						  =HubertConfig()
   snake_case   :							List[str]						  =model.config

   snake_case   :							List[str]						  =fs_config.activation_dropout
   snake_case   :							List[Any]						  =False
   snake_case   :							Optional[Any]						  =fs_config.attention_dropout
   snake_case   :							Optional[int]						  =False
   snake_case   :							Any						  =eval(fs_config.extractor_conv_feature_layers							)
   snake_case   :							Optional[int]						  =[x[0] for x in conv_layers]
   snake_case   :							Any						  =[x[1] for x in conv_layers]
   snake_case   :							Optional[Any]						  =[x[2] for x in conv_layers]
   snake_case   :							Optional[Any]						  ='''gelu'''
   snake_case   :							Optional[Any]						  ='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   snake_case   :							Optional[int]						  =False
   snake_case   :							Optional[Any]						  =0.0
   snake_case   :							Optional[int]						  =0.0
   snake_case   :							Dict						  =fs_config.activation_fn
   snake_case   :							Any						  =fs_config.dropout
   snake_case   :							Optional[int]						  =fs_config.encoder_embed_dim
   snake_case   :							Dict						  =0.02
   snake_case   :							Dict						  =fs_config.encoder_ffn_embed_dim
   snake_case   :							str						  =1e-5
   snake_case   :							List[Any]						  =0.0
   snake_case   :							Dict						  =fs_config.encoder_attention_heads
   snake_case   :							Union[str, Any]						  =fs_config.conv_pos_groups
   snake_case   :							Union[str, Any]						  =fs_config.conv_pos
   snake_case   :							Dict						  =len(lowerCamelCase_							)
   snake_case   :							str						  =fs_config.encoder_layers

   return config
@torch.no_grad()
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_=None							):
   snake_case   :							Union[str, Any]						  =distilhubert().model.model

   if config_path is not None:
      snake_case   :							int						  =HubertConfig.from_pretrained(lowerCamelCase_							)
   else:
      snake_case   :							Optional[Any]						  =convert_config(lowerCamelCase_							)
   snake_case   :							Tuple						  =model.eval()

   snake_case   :							Optional[Any]						  =WavaVecaFeatureExtractor(
       feature_size=1					,						sampling_rate=1_60_00					,						padding_value=0					,						do_normalize=lowerCamelCase_					,						return_attention_mask=lowerCamelCase_					,						)
   snake_case   :							Any						  =HubertModel(lowerCamelCase_							)

   recursively_load_weights(lowerCamelCase_					,						lowerCamelCase_							)

   feature_extractor.save_pretrained(lowerCamelCase_							)
   hf_model.save_pretrained(lowerCamelCase_							)


if __name__ == "__main__":
      A			:   Tuple			=      argparse.ArgumentParser()
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      A			:   int			=      parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





