






"""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()
__a       :	Tuple             = logging.get_logger(__name__)

__a       :	int             = {
    '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',
}
__a       :	Tuple             = [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=None):
					# load the pre-trained checkpoints
					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__":
	__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('--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')
	__a       :	Any             = parser.parse_args()
	convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










"""simple docstring"""







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

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


if is_sentencepiece_available():
	from .tokenization_albert import AlbertTokenizer
else:
	__a       :	int             = None

__a       :	List[str]             = logging.get_logger(__name__)
__a       :	Union[str, Any]             = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__a       :	str             = {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

__a       :	Optional[int]             = {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

__a       :	Union[str, Any]             = '▁'







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =VOCAB_FILES_NAMES
							_SCREAMING_SNAKE_CASE    =PRETRAINED_VOCAB_FILES_MAP
							_SCREAMING_SNAKE_CASE    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_SCREAMING_SNAKE_CASE    =AlbertTokenizer




							def __init__(   self:     List[Any]				,		__A:     List[Any]=None				,		__A:     Tuple=None				,		__A:     Union[str, Any]=True				,		__A:     int=True				,		__A:     Optional[int]=False				,		__A:     List[Any]="[CLS]"				,		__A:     Optional[int]="[SEP]"				,		__A:     Union[str, Any]="<unk>"				,		__A:     int="[SEP]"				,		__A:     Optional[Any]="<pad>"				,		__A:     Optional[int]="[CLS]"				,		__A:     str="[MASK]"				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												a__          =  (
												    AddedToken(__A				,		lstrip=__A				,		rstrip=__A				,		normalized=__A    )
												    if isinstance(__A				,		__A    )
												    else mask_token
												)

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

												a__          =  do_lower_case
												a__          =  remove_space
												a__          =  keep_accents
												a__          =  vocab_file
												a__          =  False if not self.vocab_file else True




							def  lowercase		(   self:     Union[str, Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     List[Any]				,		__A:     List[int]				,		__A:     Optional[List[int]] = None    ):

												'''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]





							def  lowercase		(   self:     Union[str, Any]				,		__A:     str				,		__A:     Optional[str] = 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(__A    ):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
																	return
												a__          =  os.path.join(
												    __A				,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

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

												return (out_vocab_file,)


