




'''simple docstring'''


import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
_snake_case				:				str       							=		logging.get_logger(__name__)

_snake_case				:				Optional[Any]       							=		{
    '''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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
_snake_case				:				Dict       							=		[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  List[str]			,						UpperCamelCase	:  str			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  Dict				):





							'''simple docstring'''


							for attribute in key.split('''.'''				):
														_a     					=    getattr(__lowerCAmelCase			,						__lowerCAmelCase				)

							if weight_type is not None:
														_a     					=    getattr(__lowerCAmelCase			,						__lowerCAmelCase				).shape
							else:
														_a     					=    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":
														_a     					=    value
							elif weight_type == "weight_g":
														_a     					=    value
							elif weight_type == "weight_v":
														_a     					=    value
							elif weight_type == "bias":
														_a     					=    value
							else:
														_a     					=    value

							logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'				)
def 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							_a     					=    []
							_a     					=    fairseq_model.state_dict()

							_a     					=    hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														_a     					=    False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						hf_model.config.feat_extract_norm == '''group'''			,						)
																					_a     					=    True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
																																			_a     					=    True
																																			if "*" in mapped_key:
																																										_a     					=    name.split(__lowerCAmelCase				)[0].split('''.'''				)[-2]
																																										_a     					=    mapped_key.replace('''*'''			,						__lowerCAmelCase				)
																																			if "weight_g" in name:
																																										_a     					=    '''weight_g'''
																																			elif "weight_v" in name:
																																										_a     					=    '''weight_v'''
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										_a     					=    '''bias'''
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										_a     					=    '''weight'''
																																			else:
																																										_a     					=    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 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Dict			,						UpperCamelCase	:  int			,						UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							_a     					=    full_name.split('''conv_layers.'''				)[-1]
							_a     					=    name.split('''.'''				)
							_a     					=    int(items[0]				)
							_a     					=    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.'
																					)
																					_a     					=    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.'
																					)
																					_a     					=    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."
																					)
																					_a     					=    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.'
																					)
																					_a     					=    value
																					logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
							else:
														unused_weights.append(__lowerCAmelCase				)
@torch.no_grad()
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Any			,						UpperCamelCase	:  List[Any]=None				):





							'''simple docstring'''


							_a     					=    torch.load(__lowerCAmelCase				)
							_a     					=    WavLMConfigOrig(checkpoint['''cfg''']				)
							_a     					=    WavLMOrig(__lowerCAmelCase				)
							model.load_state_dict(checkpoint['''model''']				)
							model.eval()

							if config_path is not None:
														_a     					=    WavLMConfig.from_pretrained(__lowerCAmelCase				)
							else:
														_a     					=    WavLMConfig()

							_a     					=    WavLMModel(__lowerCAmelCase				)

							recursively_load_weights(__lowerCAmelCase			,						__lowerCAmelCase				)

							hf_wavlm.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
				_snake_case				:				Union[str, Any]       							=		argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				_snake_case				:				Union[str, Any]       							=		parser.parse_args()
				convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_snake_case				:				str       							=		logging.get_logger(__name__)

_snake_case				:				Tuple       							=		{'vocab_file': 'spiece.model'}

_snake_case				:				Optional[int]       							=		{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

_snake_case				:				Tuple       							=		{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}

class       A   (		_a   ):
							lowercase_          =    VOCAB_FILES_NAMES
							lowercase_          =    PRETRAINED_VOCAB_FILES_MAP
							lowercase_          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_          =    ['input_ids', 'attention_mask']
							lowercase_          =    []







							def __init__(  self	:  Optional[Any]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[int]="<unk>"							,      lowerCAmelCase_	:  Tuple="<s>"							,      lowerCAmelCase_	:  List[str]="</s>"							,      lowerCAmelCase_	:  int="<pad>"							,      lowerCAmelCase_	:  Union[str, Any]="[SEP]"							,      lowerCAmelCase_	:  Dict="[MASK]"							,      lowerCAmelCase_	:  Optional[int]="[CLS]"							,      lowerCAmelCase_	:  Optional[Dict[str, Any]] = None							,      **lowerCAmelCase_	:  Union[str, Any]							,      )      ->							None:


														"""simple docstring"""


														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else bos_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else eos_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else unk_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else pad_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else cls_token
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else sep_token

														# Mask token behave like a normal word, i.e. include the space before it
														_a     					=    AddedToken(lowerCAmelCase_							,      lstrip=lowerCAmelCase_							,      rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ) else mask_token

														_a     					=    {} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=lowerCAmelCase_							,      eos_token=lowerCAmelCase_							,      unk_token=lowerCAmelCase_							,      pad_token=lowerCAmelCase_							,      sep_token=lowerCAmelCase_							,      mask_token=lowerCAmelCase_							,      cls_token=lowerCAmelCase_							,      sp_model_kwargs=self.sp_model_kwargs							,      **lowerCAmelCase_							,      )

														_a     					=    vocab_file

														_a     					=    spm.SentencePieceProcessor(**self.sp_model_kwargs )
														self.sp_model.Load(lowerCAmelCase_ )







							@property
							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							int:


														"""simple docstring"""


														return self.sp_model.get_piece_size()







							def        __lowerCAmelCase      (  self	:  str )      ->							List[str]:


														"""simple docstring"""


														_a     					=    {self.convert_ids_to_tokens(lowerCAmelCase_ ): i for i in range(self.vocab_size )}
														vocab.update(self.added_tokens_encoder )
														return vocab







							def __getstate__(  self	:  str )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.__dict__.copy()
														_a     					=    None
														return state







							def __setstate__(  self	:  List[str]							,      lowerCAmelCase_	:  Any )      ->							Dict:


														"""simple docstring"""


														_a     					=    d

														# for backward compatibility
														if not hasattr(self							,      '''sp_model_kwargs''' ):
																					_a     					=    {}

														_a     					=    spm.SentencePieceProcessor(**self.sp_model_kwargs )
														self.sp_model.Load(self.vocab_file )







							def        __lowerCAmelCase      (  self	:  Optional[int]							,      lowerCAmelCase_	:  str )      ->							List[str]:


														"""simple docstring"""


														return self.sp_model.encode(lowerCAmelCase_							,      out_type=lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  List[str] )      ->							int:


														"""simple docstring"""


														return self.sp_model.piece_to_id(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    self.sp_model.IdToPiece(lowerCAmelCase_ )
														return token







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  Union[str, Any] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    []
														_a     					=    ''''''
														_a     					=    False
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												if not prev_is_special:
																																			out_string += " "
																												out_string += self.sp_model.decode(lowerCAmelCase_ ) + token
																												_a     					=    True
																												_a     					=    []
																					else:
																												current_sub_tokens.append(lowerCAmelCase_ )
																												_a     					=    False
														out_string += self.sp_model.decode(lowerCAmelCase_ )
														return out_string.strip()







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  bool = False							,      lowerCAmelCase_	:  bool = None							,      lowerCAmelCase_	:  bool = True							,      **lowerCAmelCase_	:  Tuple							,      )      ->							str:


														"""simple docstring"""


														_a     					=    kwargs.pop('''use_source_tokenizer'''							,      lowerCAmelCase_ )

														_a     					=    self.convert_ids_to_tokens(lowerCAmelCase_							,      skip_special_tokens=lowerCAmelCase_ )

														# To avoid mixing byte-level and unicode for byte-level BPT
														# we need to build string separately for added tokens and byte-level tokens
														# cf. https://github.com/huggingface/transformers/issues/1133
														_a     					=    []
														_a     					=    []
														for token in filtered_tokens:
																					if skip_special_tokens and token in self.all_special_ids:
																												continue
																					if token in self.added_tokens_encoder:
																												if current_sub_text:
																																			sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_ ) )
																																			_a     					=    []
																												sub_texts.append(lowerCAmelCase_ )
																					else:
																												current_sub_text.append(lowerCAmelCase_ )
														if current_sub_text:
																					sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_ ) )

														# Mimic the behavior of the Rust tokenizer:
														# No space before [MASK] and [SEP]
														if spaces_between_special_tokens:
																					_a     					=    re.sub(R''' (\[(MASK|SEP)\])'''							,      R'''\1'''							,      ''' '''.join(lowerCAmelCase_ ) )
														else:
																					_a     					=    ''''''.join(lowerCAmelCase_ )

														_a     					=    (
														    clean_up_tokenization_spaces
														    if clean_up_tokenization_spaces is not None
														    else self.clean_up_tokenization_spaces
														)
														if clean_up_tokenization_spaces:
																					_a     					=    self.clean_up_tokenization(lowerCAmelCase_ )
																					return clean_text
														else:
																					return text







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Optional[str] = None )      ->							Tuple[str]:


														"""simple docstring"""


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

														if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase_ ) and os.path.isfile(self.vocab_file ):
																					copyfile(self.vocab_file							,      lowerCAmelCase_ )
														elif not os.path.isfile(self.vocab_file ):
																					with open(lowerCAmelCase_							,      '''wb''' ) as fi:
																												_a     					=    self.sp_model.serialized_model_proto()
																												fi.write(lowerCAmelCase_ )

														return (out_vocab_file,)







							def        __lowerCAmelCase      (  self	:  int							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None )      ->							List[int]:


														"""simple docstring"""


														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														_a     					=    [self.cls_token_id]
														_a     					=    [self.sep_token_id]
														return cls + token_ids_a + sep + token_ids_a + sep







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None							,      lowerCAmelCase_	:  bool = False )      ->							List[int]:


														"""simple docstring"""


														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=lowerCAmelCase_							,      token_ids_a=lowerCAmelCase_							,      already_has_special_tokens=lowerCAmelCase_ )

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







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  List[int]							,      lowerCAmelCase_	:  Optional[List[int]] = None )      ->							List[int]:


														"""simple docstring"""


														_a     					=    [self.sep_token_id]
														_a     					=    [self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep ) * [0]
														return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]

