



'''simple docstring'''





from __future__ import annotations

import random
import string





class 							lowerCAmelCase_		:





				def __init__(    self    :    Union[str, Any],    _snake_case    :    str | None = None     ):



							'''simple docstring'''







							snake_case   :							Optional[int]						  =passcode or self.__passcode_creator()
							snake_case   :							List[str]						  =self.__make_key_list()
							snake_case   :							Dict						  =self.__make_shift_key()





				def __str__(    self    :    List[Any]     ):



							'''simple docstring'''







							return "".join(self.__passcode     )





				def 					__snake_case       (    self    :    int,    _snake_case    :    list[int]     ):



							'''simple docstring'''







							for i in range(1,    len(_snake_case     ),    2     ):
										iterlist[i] *= -1
							return iterlist





				def 					__snake_case       (    self    :    Optional[int]     ):



							'''simple docstring'''







							snake_case   :							int						  =string.ascii_letters + string.digits
							snake_case   :							Union[str, Any]						  =[random.choice(_snake_case     ) for _ in range(random.randint(10,    20     )     )]
							return password





				def 					__snake_case       (    self    :    Optional[int]     ):



							'''simple docstring'''







							snake_case   :							Dict						  =(
							    string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
							)

							snake_case   :							Any						  =[]

							# creates points known as breakpoints to break the key_list_options at those
							# points and pivot each substring
							snake_case   :							Optional[int]						  =sorted(set(self.__passcode     )     )
							snake_case   :							list[str]						  =[]

							# algorithm for creating a new shuffled list, keys_l, out of key_list_options
							for i in key_list_options:
										temp_list.extend(_snake_case     )

										# checking breakpoints at which to pivot temporary sublist and add it into
										# keys_l
										if i in breakpoints or i == key_list_options[-1]:
													keys_l.extend(temp_list[::-1]     )
													temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
							return keys_l





				def 					__snake_case       (    self    :    Union[str, Any]     ):



							'''simple docstring'''







							snake_case   :							str						  =sum(self.__neg_pos([ord(_snake_case     ) for x in self.__passcode]     )     )
							return num if num > 0 else len(self.__passcode     )





				def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    str     ):



							'''simple docstring'''







							snake_case   :							List[Any]						  =''''''

							# decoding shift like Caesar cipher algorithm implementing negative shift or
							# reverse shift or left shift
							for i in encoded_message:
										snake_case   :							Optional[int]						  =self.__key_list.index(_snake_case     )
										decoded_message += self.__key_list[
										    (position - self.__shift_key) % -len(self.__key_list     )
										]

							return decoded_message






				def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    str     ):



							'''simple docstring'''







							snake_case   :							int						  =''''''

							# encoding shift like Caesar cipher algorithm implementing positive shift or
							# forward shift or right shift
							for i in plaintext:
										snake_case   :							int						  =self.__key_list.index(_snake_case     )
										encoded_message += self.__key_list[
										    (position + self.__shift_key) % len(self.__key_list     )
										]

							return encoded_message




def 		_a   (  lowerCamelCase_ = "Hello, this is a modified Caesar cipher"							):
			snake_case   :							List[Any]						  =ShuffledShiftCipher()
			return cipa.decrypt(cipa.encrypt(lowerCamelCase_							)							)


if __name__ == "__main__":
						import doctest

						doctest.testmod()










'''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)





