




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     =			logging.get_logger(__name__)

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





def __UpperCamelCase			(       _A ,  _A ,  _A ,  _A ,  _A  ):
						for attribute in key.split('''.'''  ):
												lowerCAmelCase_      			=  getattr(_A ,  _A  )

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

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





def __UpperCamelCase			(       _A ,  _A  ):
						lowerCAmelCase_      			=  []
						lowerCAmelCase_      			=  fairseq_model.state_dict()

						lowerCAmelCase_      			=  hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												lowerCAmelCase_      			=  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _A ,  _A ,  _A ,  _A ,  hf_model.config.feat_extract_norm == '''group''' ,  )
																		lowerCAmelCase_      			=  True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split('''w2v_model.'''  )[-1] == name.split('''.'''  )[0]:
																														lowerCAmelCase_      			=  True
																														if "*" in mapped_key:
																																				lowerCAmelCase_      			=  name.split(_A  )[0].split('''.'''  )[-2]
																																				lowerCAmelCase_      			=  mapped_key.replace('''*''' ,  _A  )
																														if "weight_g" in name:
																																				lowerCAmelCase_      			=  '''weight_g'''
																														elif "weight_v" in name:
																																				lowerCAmelCase_      			=  '''weight_v'''
																														elif "bias" in name and "relative_attention_bias" not in name:
																																				lowerCAmelCase_      			=  '''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowerCAmelCase_      			=  '''weight'''
																														else:
																																				lowerCAmelCase_      			=  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 __UpperCamelCase			(       _A ,  _A ,  _A ,  _A ,  _A  ):
						lowerCAmelCase_      			=  full_name.split('''conv_layers.'''  )[-1]
						lowerCAmelCase_      			=  name.split('''.'''  )
						lowerCAmelCase_      			=  int(items[0]  )
						lowerCAmelCase_      			=  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_      			=  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_      			=  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_      			=  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_      			=  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 __UpperCamelCase			(       _A ,  _A ,  _A=None  ):
						# load the pre-trained checkpoints
						lowerCAmelCase_      			=  torch.load(_A  )
						lowerCAmelCase_      			=  WavLMConfigOrig(checkpoint['''cfg''']  )
						lowerCAmelCase_      			=  WavLMOrig(_A  )
						model.load_state_dict(checkpoint['''model''']  )
						model.eval()

						if config_path is not None:
												lowerCAmelCase_      			=  WavLMConfig.from_pretrained(_A  )
						else:
												lowerCAmelCase_      			=  WavLMConfig()

						lowerCAmelCase_      			=  WavLMModel(_A  )

						recursively_load_weights(_A ,  _A  )

						hf_wavlm.save_pretrained(_A  )


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










import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


_A     =			logging.get_logger(__name__)


class  A		(  __UpperCAmelCase     ):



		def __init__(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								warnings.warn(
								    '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use BeitImageProcessor instead.''',	UpperCamelCase__,	)
								super().__init__(*UpperCamelCase__,	**UpperCamelCase__   )




