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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






       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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Any:




       '''simple docstring'''






       _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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[Any]:




       '''simple docstring'''






       _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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_=None   )		->						int:




       '''simple docstring'''






       _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__":
      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)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		="""speech_to_text_2"""
     _SCREAMING_SNAKE_CASE     :     str		=["""past_key_values"""]
     _SCREAMING_SNAKE_CASE     :     Tuple		={"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}



     def __init__(					self				,      lowerCAmelCase__=10000				,      lowerCAmelCase__=6				,      lowerCAmelCase__=2048				,      lowerCAmelCase__=4				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=True				,      lowerCAmelCase__="relu"				,      lowerCAmelCase__=256				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=2				,      lowerCAmelCase__=True				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=1024				,      **lowerCAmelCase__				,      ):
            _A=	vocab_size
            _A=	d_model
            _A=	decoder_ffn_dim
            _A=	decoder_layers
            _A=	decoder_attention_heads
            _A=	dropout
            _A=	attention_dropout
            _A=	activation_dropout
            _A=	activation_function
            _A=	init_std
            _A=	decoder_layerdrop
            _A=	use_cache
            _A=	decoder_layers
            _A=	scale_embedding  # scale factor will be sqrt(d_model) if True
            _A=	max_target_positions

            super().__init__(
                pad_token_id=lowerCAmelCase__				,      bos_token_id=lowerCAmelCase__				,      eos_token_id=lowerCAmelCase__				,      decoder_start_token_id=lowerCAmelCase__				,      **lowerCAmelCase__				,      )