



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     :    List[Any]      						=					logging.get_logger(__name__)

a     :    List[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""",
}
a     :    Optional[Any]      						=					[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    Union[str, Any]:
							for attribute in key.split("""."""	):
														UpperCAmelCase  :	Optional[Any]							  =	getattr(_lowercase			,       _lowercase	)

							if weight_type is not None:
														UpperCAmelCase  :	Any							  =	getattr(_lowercase			,       _lowercase	).shape
							else:
														UpperCAmelCase  :	Optional[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":
														UpperCAmelCase  :	Dict							  =	value
							elif weight_type == "weight_g":
														UpperCAmelCase  :	Tuple							  =	value
							elif weight_type == "weight_v":
														UpperCAmelCase  :	Dict							  =	value
							elif weight_type == "bias":
														UpperCAmelCase  :	int							  =	value
							else:
														UpperCAmelCase  :	Tuple							  =	value

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	int							  =	[]
							UpperCAmelCase  :	Any							  =	fairseq_model.state_dict()

							UpperCAmelCase  :	Union[str, Any]							  =	hf_model.feature_extractor

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

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

							logger.warning(F'''Unused weights: {unused_weights}'''	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	Dict							  =	full_name.split("""conv_layers."""	)[-1]
							UpperCAmelCase  :	Any							  =	name.split("""."""	)
							UpperCAmelCase  :	Optional[int]							  =	int(items[0]	)
							UpperCAmelCase  :	Dict							  =	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.'''
																					)
																					UpperCAmelCase  :	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.'''
																					)
																					UpperCAmelCase  :	Dict							  =	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."
																					)
																					UpperCAmelCase  :	str							  =	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.'''
																					)
																					UpperCAmelCase  :	Optional[int]							  =	value
																					logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
							else:
														unused_weights.append(_lowercase	)



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=None	)   ->    Dict:
							# load the pre-trained checkpoints
							UpperCAmelCase  :	Optional[Any]							  =	torch.load(_lowercase	)
							UpperCAmelCase  :	List[str]							  =	WavLMConfigOrig(checkpoint["""cfg"""]	)
							UpperCAmelCase  :	Union[str, Any]							  =	WavLMOrig(_lowercase	)
							model.load_state_dict(checkpoint["""model"""]	)
							model.eval()

							if config_path is not None:
														UpperCAmelCase  :	Union[str, Any]							  =	WavLMConfig.from_pretrained(_lowercase	)
							else:
														UpperCAmelCase  :	List[Any]							  =	WavLMConfig()

							UpperCAmelCase  :	Optional[Any]							  =	WavLMModel(_lowercase	)

							recursively_load_weights(_lowercase			,       _lowercase	)

							hf_wavlm.save_pretrained(_lowercase	)


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






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a     :    Optional[Any]      						=					{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Dict      						=					["""BertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[Any]      						=					[
							    """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BertForMaskedLM""",
							    """BertForMultipleChoice""",
							    """BertForNextSentencePrediction""",
							    """BertForPreTraining""",
							    """BertForQuestionAnswering""",
							    """BertForSequenceClassification""",
							    """BertForTokenClassification""",
							    """BertLayer""",
							    """BertLMHeadModel""",
							    """BertModel""",
							    """BertPreTrainedModel""",
							    """load_tf_weights_in_bert""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    List[str]      						=					[
							    """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFBertEmbeddings""",
							    """TFBertForMaskedLM""",
							    """TFBertForMultipleChoice""",
							    """TFBertForNextSentencePrediction""",
							    """TFBertForPreTraining""",
							    """TFBertForQuestionAnswering""",
							    """TFBertForSequenceClassification""",
							    """TFBertForTokenClassification""",
							    """TFBertLMHeadModel""",
							    """TFBertMainLayer""",
							    """TFBertModel""",
							    """TFBertPreTrainedModel""",
							]
try:
							if not is_tensorflow_text_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    int      						=					["""TFBertTokenizer"""]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Optional[Any]      						=					[
							    """FlaxBertForCausalLM""",
							    """FlaxBertForMaskedLM""",
							    """FlaxBertForMultipleChoice""",
							    """FlaxBertForNextSentencePrediction""",
							    """FlaxBertForPreTraining""",
							    """FlaxBertForQuestionAnswering""",
							    """FlaxBertForSequenceClassification""",
							    """FlaxBertForTokenClassification""",
							    """FlaxBertModel""",
							    """FlaxBertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
							from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_fast import BertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert import (
														    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BertForMaskedLM,
														    BertForMultipleChoice,
														    BertForNextSentencePrediction,
														    BertForPreTraining,
														    BertForQuestionAnswering,
														    BertForSequenceClassification,
														    BertForTokenClassification,
														    BertLayer,
														    BertLMHeadModel,
														    BertModel,
														    BertPreTrainedModel,
														    load_tf_weights_in_bert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_bert import (
														    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFBertEmbeddings,
														    TFBertForMaskedLM,
														    TFBertForMultipleChoice,
														    TFBertForNextSentencePrediction,
														    TFBertForPreTraining,
														    TFBertForQuestionAnswering,
														    TFBertForSequenceClassification,
														    TFBertForTokenClassification,
														    TFBertLMHeadModel,
														    TFBertMainLayer,
														    TFBertModel,
														    TFBertPreTrainedModel,
														)

							try:
														if not is_tensorflow_text_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_tf import TFBertTokenizer

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_bert import (
														    FlaxBertForCausalLM,
														    FlaxBertForMaskedLM,
														    FlaxBertForMultipleChoice,
														    FlaxBertForNextSentencePrediction,
														    FlaxBertForPreTraining,
														    FlaxBertForQuestionAnswering,
														    FlaxBertForSequenceClassification,
														    FlaxBertForTokenClassification,
														    FlaxBertModel,
														    FlaxBertPreTrainedModel,
														)

else:
							import sys

							a     :    Optional[int]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

