





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()
_UpperCamelCase							=      logging.get_logger(__name__)

_UpperCamelCase							=      {
    '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',
}
_UpperCamelCase							=      [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Tuple    ,						SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							List[Any]   ):


							'''simple docstring'''







							for attribute in key.split('''.'''   ):
														__lowerCamelCase      :      Union[str, Any]         =getattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							if weight_type is not None:
														__lowerCamelCase      :      Tuple         =getattr(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ).shape
							else:
														__lowerCamelCase      :      str         =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":
														__lowerCamelCase      :      Any         =value
							elif weight_type == "weight_g":
														__lowerCamelCase      :      Optional[int]         =value
							elif weight_type == "weight_v":
														__lowerCamelCase      :      Any         =value
							elif weight_type == "bias":
														__lowerCamelCase      :      str         =value
							else:
														__lowerCamelCase      :      Optional[int]         =value

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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =[]
							__lowerCamelCase      :      List[str]         =fairseq_model.state_dict()

							__lowerCamelCase      :      Dict         =hf_model.feature_extractor

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

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

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



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							List[Any]   ):


							'''simple docstring'''







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



@torch.no_grad()
def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							Optional[int]=None   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =torch.load(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Dict         =WavLMConfigOrig(checkpoint['''cfg''']   )
							__lowerCamelCase      :      Optional[int]         =WavLMOrig(SCREAMING_SNAKE_CASE   )
							model.load_state_dict(checkpoint['''model''']   )
							model.eval()

							if config_path is not None:
														__lowerCamelCase      :      Optional[int]         =WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE   )
							else:
														__lowerCamelCase      :      Optional[int]         =WavLMConfig()

							__lowerCamelCase      :      Dict         =WavLMModel(SCREAMING_SNAKE_CASE   )

							recursively_load_weights(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							hf_wavlm.save_pretrained(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
							_UpperCamelCase							=      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')
							_UpperCamelCase							=      parser.parse_args()
							convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)











"""simple docstring"""





def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							list    ,						SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =len(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Union[str, Any]         =[[0] * n for i in range(SCREAMING_SNAKE_CASE   )]
							for i in range(SCREAMING_SNAKE_CASE   ):
														__lowerCamelCase      :      Union[str, Any]         =y_points[i]

							for i in range(2    ,						SCREAMING_SNAKE_CASE   ):
														for j in range(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =(
																					    (xa - x_points[j - i + 1]) * q[j][i - 1]
																					    - (xa - x_points[j]) * q[j - 1][i - 1]
																					) / (x_points[j] - x_points[j - i + 1])

							return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()




