



'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


A			:   Optional[Any]			=      logging.get_logger(__name__)

A			:   Any			=      {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

A			:   Union[str, Any]			=      {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}





class 							lowerCAmelCase_		(	_UpperCAmelCase						):





				def __init__(    self    :    Any,    _snake_case    :    Tuple=None,    _snake_case    :    List[str]=None,    _snake_case    :    Any=False,    _snake_case    :    Tuple=True,    _snake_case    :    str=False,    _snake_case    :    Tuple="<s>",    _snake_case    :    Any="</s>",    _snake_case    :    Dict="<unk>",    _snake_case    :    Any="<sep>",    _snake_case    :    Union[str, Any]="<pad>",    _snake_case    :    str="<cls>",    _snake_case    :    List[Any]="<mask>",    _snake_case    :    Union[str, Any]=["<eop>", "<eod>"],    **_snake_case    :    Dict,    ):



							'''simple docstring'''







							snake_case   :							Tuple						  =AddedToken(lowercase__,    lstrip=lowercase__,    rstrip=lowercase__     ) if isinstance(lowercase__,    lowercase__     ) else mask_token

							super().__init__(
							    vocab_file=lowercase__,    tokenizer_file=lowercase__,    do_lower_case=lowercase__,    remove_space=lowercase__,    keep_accents=lowercase__,    bos_token=lowercase__,    eos_token=lowercase__,    unk_token=lowercase__,    sep_token=lowercase__,    pad_token=lowercase__,    cls_token=lowercase__,    mask_token=lowercase__,    additional_special_tokens=lowercase__,    **lowercase__,    )

							snake_case   :							List[str]						  =3
							snake_case   :							Optional[int]						  =do_lower_case
							snake_case   :							Optional[int]						  =remove_space
							snake_case   :							Union[str, Any]						  =keep_accents
							snake_case   :							List[str]						  =vocab_file
							snake_case   :							List[Any]						  =False if not self.vocab_file else True

							try:
										import jieba
							except ModuleNotFoundError as error:
										raise error.__class__(
										    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
										    '''See https://pypi.org/project/jieba/ for installation.'''     )
							snake_case   :							Dict						  =jieba
							snake_case   :							Optional[Any]						  =str.maketrans(''' \n''',    '''\u2582\u2583'''     )





				def 					__snake_case       (    self    :    Union[str, Any],    _snake_case    :    Optional[Any],    _snake_case    :    Optional[Any] = None     ):



							'''simple docstring'''







							snake_case   :							Union[str, Any]						  =[self.sep_token_id]
							snake_case   :							List[Any]						  =[self.cls_token_id]
							if token_ids_a is None:
										return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls





				def 					__snake_case       (    self    :    Any,    _snake_case    :    Tuple,    _snake_case    :    List[str] = None     ):



							'''simple docstring'''







							snake_case   :							List[Any]						  =[self.sep_token_id]
							snake_case   :							Dict						  =[2]

							if token_ids_a is None:
										return len(token_ids_a + sep     ) * [0] + cls_segment_id
							return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id





				def 					__snake_case       (    self    :    Tuple,    _snake_case    :    int,    _snake_case    :    int = None     ):



							'''simple docstring'''







							if not self.can_save_slow_tokenizer:
										raise ValueError(
										    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
										    '''tokenizer.'''     )

							if not os.path.isdir(lowercase__     ):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
										return
							snake_case   :							str						  =os.path.join(
							    lowercase__,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

							if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowercase__     ):
										copyfile(self.vocab_file,    lowercase__     )

							return (out_vocab_file,)





				def 					__snake_case       (    self    :    Tuple,    _snake_case    :    Optional[int],    *_snake_case    :    Dict,    **_snake_case    :    List[str]     ):



							'''simple docstring'''







							snake_case   :							int						  =[
							    " ".join([x.translate(self.translator     ) for x in self.jieba.cut(lowercase__,    cut_all=lowercase__     )]     )
							    for text in batch_text_or_text_pairs
							]
							return super()._batch_encode_plus(lowercase__,    *lowercase__,    **lowercase__     )






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



							'''simple docstring'''







							snake_case   :							Dict						  =super()._decode(*lowercase__,    **lowercase__     )
							snake_case   :							str						  =text.replace(''' ''',    ''''''     ).replace('''\u2582''',    ''' '''     ).replace('''\u2583''',    '''\n'''     )
							return text










'''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)





