

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

lowerCAmelCase							:  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.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""",
}
lowerCAmelCase							:  List[str]            =						[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):

						for attribute in key.split("."   ):
												SCREAMING_SNAKE_CASE_:			Tuple   		=	getattr(a__    ,     a__   )

						if weight_type is not None:
												SCREAMING_SNAKE_CASE_:			List[Any]   		=	getattr(a__    ,     a__   ).shape
						else:
												SCREAMING_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":
												SCREAMING_SNAKE_CASE_:			List[str]   		=	value
						elif weight_type == "weight_g":
												SCREAMING_SNAKE_CASE_:			Any   		=	value
						elif weight_type == "weight_v":
												SCREAMING_SNAKE_CASE_:			List[str]   		=	value
						elif weight_type == "bias":
												SCREAMING_SNAKE_CASE_:			Union[str, Any]   		=	value
						else:
												SCREAMING_SNAKE_CASE_:			Dict   		=	value

						logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Tuple   		=	[]
						SCREAMING_SNAKE_CASE_:			Dict   		=	fairseq_model.state_dict()

						SCREAMING_SNAKE_CASE_:			Optional[int]   		=	hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												SCREAMING_SNAKE_CASE_:			Optional[int]   		=	False
												if "conv_layers" in name:
																		load_conv_layer(
																		    a__    ,     a__    ,     a__    ,     a__    ,     hf_model.config.feat_extract_norm == "group"    ,     )
																		SCREAMING_SNAKE_CASE_:			Dict   		=	True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split("w2v_model."   )[-1] == name.split("."   )[0]:
																														SCREAMING_SNAKE_CASE_:			List[Any]   		=	True
																														if "*" in mapped_key:
																																				SCREAMING_SNAKE_CASE_:			Any   		=	name.split(a__   )[0].split("."   )[-2]
																																				SCREAMING_SNAKE_CASE_:			str   		=	mapped_key.replace("*"    ,     a__   )
																														if "weight_g" in name:
																																				SCREAMING_SNAKE_CASE_:			Union[str, Any]   		=	"weight_g"
																														elif "weight_v" in name:
																																				SCREAMING_SNAKE_CASE_:			int   		=	"weight_v"
																														elif "bias" in name and "relative_attention_bias" not in name:
																																				SCREAMING_SNAKE_CASE_:			str   		=	"bias"
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				SCREAMING_SNAKE_CASE_:			Optional[Any]   		=	"weight"
																														else:
																																				SCREAMING_SNAKE_CASE_:			List[str]   		=	None

																														set_recursively(a__    ,     a__    ,     a__    ,     a__    ,     a__   )
																								continue
												if not is_used:
																		unused_weights.append(a__   )

						logger.warning(f"Unused weights: {unused_weights}"   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[Any]   		=	full_name.split("conv_layers."   )[-1]
						SCREAMING_SNAKE_CASE_:			Optional[Any]   		=	name.split("."   )
						SCREAMING_SNAKE_CASE_:			Optional[int]   		=	int(items[0]   )
						SCREAMING_SNAKE_CASE_:			Any   		=	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."
																		)
																		SCREAMING_SNAKE_CASE_:			Any   		=	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."
																		)
																		SCREAMING_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."
																		)
																		SCREAMING_SNAKE_CASE_:			Dict   		=	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."
																		)
																		SCREAMING_SNAKE_CASE_:			Tuple   		=	value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
						else:
												unused_weights.append(a__   )







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=None   ):
						SCREAMING_SNAKE_CASE_:			int   		=	torch.load(a__   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]   		=	WavLMConfigOrig(checkpoint["cfg"]   )
						SCREAMING_SNAKE_CASE_:			str   		=	WavLMOrig(a__   )
						model.load_state_dict(checkpoint["model"]   )
						model.eval()

						if config_path is not None:
												SCREAMING_SNAKE_CASE_:			Union[str, Any]   		=	WavLMConfig.from_pretrained(a__   )
						else:
												SCREAMING_SNAKE_CASE_:			Dict   		=	WavLMConfig()

						SCREAMING_SNAKE_CASE_:			Any   		=	WavLMModel(a__   )

						recursively_load_weights(a__    ,     a__   )

						hf_wavlm.save_pretrained(a__   )


if __name__ == "__main__":
						lowerCAmelCase							:  List[str]            =						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""")
						lowerCAmelCase							:  Union[str, Any]            =						parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowerCAmelCase							:  Any            =						logging.get_logger(__name__)

lowerCAmelCase							:  Dict            =						{"""vocab_file""": """vocab.txt"""}

lowerCAmelCase							:  List[str]            =						{
    """vocab_file""": {
        """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
        """YituTech/conv-bert-medium-small""": (
            """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
        ),
        """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
    }
}

lowerCAmelCase							:  List[Any]            =						{
    """YituTech/conv-bert-base""": 512,
    """YituTech/conv-bert-medium-small""": 512,
    """YituTech/conv-bert-small""": 512,
}


lowerCAmelCase							:  Tuple            =						{
    """YituTech/conv-bert-base""": {"""do_lower_case""": True},
    """YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
    """YituTech/conv-bert-small""": {"""do_lower_case""": True},
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							str      							=					VOCAB_FILES_NAMES
			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase       :							List[Any]      							=					PRETRAINED_INIT_CONFIGURATION
			_UpperCAmelCase       :							Optional[Any]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase       :							Dict      							=					ConvBertTokenizer



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[int]=None       ,						lowerCAmelCase__  :					List[str]=None       ,						lowerCAmelCase__  :					str=True       ,						lowerCAmelCase__  :					Any="[UNK]"       ,						lowerCAmelCase__  :					Optional[Any]="[SEP]"       ,						lowerCAmelCase__  :					Any="[PAD]"       ,						lowerCAmelCase__  :					Dict="[CLS]"       ,						lowerCAmelCase__  :					Dict="[MASK]"       ,						lowerCAmelCase__  :					Tuple=True       ,						lowerCAmelCase__  :					str=None       ,						**lowerCAmelCase__  :					Dict       ,						):
									super().__init__(
									    lowerCAmelCase__       ,						tokenizer_file=lowerCAmelCase__       ,						do_lower_case=lowerCAmelCase__       ,						unk_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						tokenize_chinese_chars=lowerCAmelCase__       ,						strip_accents=lowerCAmelCase__       ,						**lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
									if (
									    normalizer_state.get("lowercase"       ,						lowerCAmelCase__) != do_lower_case
									    or normalizer_state.get("strip_accents"       ,						lowerCAmelCase__) != strip_accents
									    or normalizer_state.get("handle_chinese_chars"       ,						lowerCAmelCase__) != tokenize_chinese_chars
									):
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	getattr(lowerCAmelCase__       ,						normalizer_state.pop("type"))
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_lower_case
															SCREAMING_SNAKE_CASE_:			List[str]  		=	strip_accents
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenize_chinese_chars
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	normalizer_class(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_lower_case



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Union[str, Any]=None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

									return output



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			int  		=	[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 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									SCREAMING_SNAKE_CASE_:			Any  		=	self._tokenizer.model.save(lowerCAmelCase__       ,						name=lowerCAmelCase__)
									return tuple(lowerCAmelCase__)






