


import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin



class 							_UpperCamelCase (						A,A						):

   '''simple docstring'''







   @register_to_config
   def __init__(  self  : Union[str, Any]       ,  _lowerCamelCase  : int = 1_2_8       ,  _lowerCamelCase  : int = 2_5_6       ,  _lowerCamelCase  : float = 2_0_0_0.0       ,  _lowerCamelCase  : int = 7_6_8       ,  _lowerCamelCase  : int = 1_2       ,  _lowerCamelCase  : int = 1_2       ,  _lowerCamelCase  : int = 6_4       ,  _lowerCamelCase  : int = 2_0_4_8       ,  _lowerCamelCase  : float = 0.1       ,  ):






         '''simple docstring'''



         super().__init__()

         __lowerCamelCase     :      int       						=						nn.Sequential(
             nn.Linear(_lowerCamelCase       ,  d_model * 4       ,  bias=_lowerCamelCase							)       ,  nn.SiLU()       ,  nn.Linear(d_model * 4       ,  d_model * 4       ,  bias=_lowerCamelCase							)       ,  nn.SiLU()       ,  )

         __lowerCamelCase     :      Optional[int]       						=						nn.Embedding(_lowerCamelCase       ,  _lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						False

         __lowerCamelCase     :      Tuple       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)

         __lowerCamelCase     :      Optional[Any]       						=						nn.Dropout(p=_lowerCamelCase							)

         __lowerCamelCase     :      int       						=						nn.ModuleList()
         for lyr_num in range(_lowerCamelCase							):
               # FiLM conditional T5 decoder
               __lowerCamelCase     :      Optional[Any]       						=						DecoderLayer(d_model=_lowerCamelCase       ,  d_kv=_lowerCamelCase       ,  num_heads=_lowerCamelCase       ,  d_ff=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase							)
               self.decoders.append(_lowerCamelCase							)

         __lowerCamelCase     :      int       						=						TaLayerNorm(_lowerCamelCase							)

         __lowerCamelCase     :      Any       						=						nn.Dropout(p=_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)

   def   _snake_case    (  self  : int       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						torch.mul(query_input.unsqueeze(-1							)       ,  key_input.unsqueeze(-2							)							)
         return mask.unsqueeze(-3							)


   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      List[str]       						=						decoder_input_tokens.shape
         assert decoder_noise_time.shape == (batch,)

         # decoder_noise_time is in [0, 1), so rescale to expected timing range.
         __lowerCamelCase     :      int       						=						get_timestep_embedding(
             decoder_noise_time * self.config.max_decoder_noise_time       ,  embedding_dim=self.config.d_model       ,  max_period=self.config.max_decoder_noise_time       ,  ).to(dtype=self.dtype							)

         __lowerCamelCase     :      Optional[int]       						=						self.conditioning_emb(_lowerCamelCase							).unsqueeze(1							)

         assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

         __lowerCamelCase     :      Optional[int]       						=						decoder_input_tokens.shape[1]

         # If we want to use relative positions for audio context, we can just offset
         # this sequence by the length of encodings_and_masks.
         __lowerCamelCase     :      List[str]       						=						torch.broadcast_to(
             torch.arange(_lowerCamelCase       ,  device=decoder_input_tokens.device							)       ,  (batch, seq_length)       ,  )

         __lowerCamelCase     :      Union[str, Any]       						=						self.position_encoding(_lowerCamelCase							)

         __lowerCamelCase     :      Dict       						=						self.continuous_inputs_projection(_lowerCamelCase							)
         inputs += position_encodings
         __lowerCamelCase     :      str       						=						self.dropout(_lowerCamelCase							)

         # decoder: No padding present.
         __lowerCamelCase     :      Any       						=						torch.ones(
             decoder_input_tokens.shape[:2]       ,  device=decoder_input_tokens.device       ,  dtype=inputs.dtype							)

         # Translate encoding masks to encoder-decoder masks.
         __lowerCamelCase     :      Union[str, Any]       						=						[(x, self.encoder_decoder_mask(_lowerCamelCase       ,  _lowerCamelCase							)) for x, y in encodings_and_masks]

         # cross attend style: concat encodings
         __lowerCamelCase     :      Optional[int]       						=						torch.cat([x[0] for x in encodings_and_encdec_masks]       ,  dim=1							)
         __lowerCamelCase     :      Dict       						=						torch.cat([x[1] for x in encodings_and_encdec_masks]       ,  dim=-1							)

         for lyr in self.decoders:
               __lowerCamelCase     :      List[str]       						=						lyr(
                   _lowerCamelCase       ,  conditioning_emb=_lowerCamelCase       ,  encoder_hidden_states=_lowerCamelCase       ,  encoder_attention_mask=_lowerCamelCase       ,  )[0]

         __lowerCamelCase     :      Optional[int]       						=						self.decoder_norm(_lowerCamelCase							)
         __lowerCamelCase     :      List[str]       						=						self.post_dropout(_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						self.spec_out(_lowerCamelCase							)
         return spec_out



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : List[str]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Union[str, Any]       ,  _lowerCamelCase  : Any=1E-6							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      List[str]       						=						nn.ModuleList()

         # cond self attention: layer 0
         self.layer.append(
             TaLayerSelfAttentionCond(d_model=_lowerCamelCase       ,  d_kv=_lowerCamelCase       ,  num_heads=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase							)							)

         # cross attention: layer 1
         self.layer.append(
             TaLayerCrossAttention(
                 d_model=_lowerCamelCase       ,  d_kv=_lowerCamelCase       ,  num_heads=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase       ,  layer_norm_epsilon=_lowerCamelCase       ,  )							)

         # Film Cond MLP + dropout: last layer
         self.layer.append(
             TaLayerFFCond(d_model=_lowerCamelCase       ,  d_ff=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase       ,  layer_norm_epsilon=_lowerCamelCase							)							)


   def   _snake_case    (  self  : Dict       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Union[str, Any]=None       ,  _lowerCamelCase  : Dict=None       ,  _lowerCamelCase  : Dict=None       ,  _lowerCamelCase  : Optional[Any]=None       ,  _lowerCamelCase  : Optional[int]=None       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						self.layer[0](
             _lowerCamelCase       ,  conditioning_emb=_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  )

         if encoder_hidden_states is not None:
               __lowerCamelCase     :      Tuple       						=						torch.where(encoder_attention_mask > 0       ,  0       ,  -1E10							).to(
                   encoder_hidden_states.dtype							)

               __lowerCamelCase     :      List[Any]       						=						self.layer[1](
                   _lowerCamelCase       ,  key_value_states=_lowerCamelCase       ,  attention_mask=_lowerCamelCase       ,  )

         # Apply Film Conditional Feed Forward layer
         __lowerCamelCase     :      List[Any]       						=						self.layer[-1](_lowerCamelCase       ,  _lowerCamelCase							)

         return (hidden_states,)



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Tuple       ,  _lowerCamelCase  : str       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      Union[str, Any]       						=						TaLayerNorm(_lowerCamelCase							)
         __lowerCamelCase     :      List[str]       						=						TaFiLMLayer(in_features=d_model * 4       ,  out_features=_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						Attention(query_dim=_lowerCamelCase       ,  heads=_lowerCamelCase       ,  dim_head=_lowerCamelCase       ,  out_bias=_lowerCamelCase       ,  scale_qk=_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						nn.Dropout(_lowerCamelCase							)


   def   _snake_case    (  self  : int       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Union[str, Any]=None       ,  _lowerCamelCase  : Optional[int]=None       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						self.layer_norm(_lowerCamelCase							)

         if conditioning_emb is not None:
               __lowerCamelCase     :      int       						=						self.FiLMLayer(_lowerCamelCase       ,  _lowerCamelCase							)

         # Self-attention block
         __lowerCamelCase     :      int       						=						self.attention(_lowerCamelCase							)

         __lowerCamelCase     :      int       						=						hidden_states + self.dropout(_lowerCamelCase							)

         return hidden_states



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Any       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Any							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      str       						=						Attention(query_dim=_lowerCamelCase       ,  heads=_lowerCamelCase       ,  dim_head=_lowerCamelCase       ,  out_bias=_lowerCamelCase       ,  scale_qk=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						TaLayerNorm(_lowerCamelCase       ,  eps=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						nn.Dropout(_lowerCamelCase							)


   def   _snake_case    (  self  : str       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : str=None       ,  _lowerCamelCase  : Optional[int]=None       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.layer_norm(_lowerCamelCase							)
         __lowerCamelCase     :      List[Any]       						=						self.attention(
             _lowerCamelCase       ,  encoder_hidden_states=_lowerCamelCase       ,  attention_mask=attention_mask.squeeze(1							)       ,  )
         __lowerCamelCase     :      Union[str, Any]       						=						hidden_states + self.dropout(_lowerCamelCase							)
         return layer_output



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Tuple       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : Any       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      List[str]       						=						TaDenseGatedActDense(d_model=_lowerCamelCase       ,  d_ff=_lowerCamelCase       ,  dropout_rate=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						TaFiLMLayer(in_features=d_model * 4       ,  out_features=_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						TaLayerNorm(_lowerCamelCase       ,  eps=_lowerCamelCase							)
         __lowerCamelCase     :      Any       						=						nn.Dropout(_lowerCamelCase							)


   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : Union[str, Any]=None							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						self.layer_norm(_lowerCamelCase							)
         if conditioning_emb is not None:
               __lowerCamelCase     :      str       						=						self.film(_lowerCamelCase       ,  _lowerCamelCase							)

         __lowerCamelCase     :      int       						=						self.DenseReluDense(_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						hidden_states + self.dropout(_lowerCamelCase							)
         return hidden_states



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Tuple       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Tuple       ,  _lowerCamelCase  : Union[str, Any]							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      int       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						nn.Linear(_lowerCamelCase       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)
         __lowerCamelCase     :      Optional[int]       						=						nn.Dropout(_lowerCamelCase							)
         __lowerCamelCase     :      Dict       						=						NewGELUActivation()


   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						self.act(self.wi_a(_lowerCamelCase							)							)
         __lowerCamelCase     :      List[str]       						=						self.wi_a(_lowerCamelCase							)
         __lowerCamelCase     :      Optional[Any]       						=						hidden_gelu * hidden_linear
         __lowerCamelCase     :      str       						=						self.dropout(_lowerCamelCase							)

         __lowerCamelCase     :      str       						=						self.wo(_lowerCamelCase							)
         return hidden_states



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Dict       ,  _lowerCamelCase  : Dict       ,  _lowerCamelCase  : Tuple=1E-6							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      int       						=						nn.Parameter(torch.ones(_lowerCamelCase							)							)
         __lowerCamelCase     :      Any       						=						eps


   def   _snake_case    (  self  : Union[str, Any]       ,  _lowerCamelCase  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						hidden_states.to(torch.floataa							).pow(2							).mean(-1       ,  keepdim=_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						hidden_states * torch.rsqrt(variance + self.variance_epsilon							)

         # convert into half-precision if necessary
         if self.weight.dtype in [torch.floataa, torch.bfloataa]:
               __lowerCamelCase     :      Dict       						=						hidden_states.to(self.weight.dtype							)

         return self.weight * hidden_states



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def   _snake_case    (  self  : Optional[Any]       ,  _lowerCamelCase  : torch.Tensor							):






         '''simple docstring'''



         return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi							) * (input + 0.044_715 * torch.pow(_lowerCamelCase       ,  3.0							))							))



class 							_UpperCamelCase (						nn.Module						):

   '''simple docstring'''







   def __init__(  self  : Any       ,  _lowerCamelCase  : int       ,  _lowerCamelCase  : str							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      List[str]       						=						nn.Linear(_lowerCamelCase       ,  out_features * 2       ,  bias=_lowerCamelCase							)


   def   _snake_case    (  self  : List[str]       ,  _lowerCamelCase  : Optional[int]       ,  _lowerCamelCase  : str							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.scale_bias(_lowerCamelCase							)
         __lowerCamelCase     :      List[Any]       						=						torch.chunk(_lowerCamelCase       ,  2       ,  -1							)
         __lowerCamelCase     :      Tuple       						=						x * (1 + scale) + shift
         return x







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    :						Any     =							logging.get_logger(__name__)

__UpperCamelCase    :						str     =							{
    '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    :						Optional[int]     =							[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Tuple      ):
      """simple docstring"""




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

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

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						[]
      __lowerCamelCase     :      int       						=						fairseq_model.state_dict()

      __lowerCamelCase     :      List[str]       						=						hf_model.feature_extractor

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

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

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




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





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]=None      ):
      """simple docstring"""




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

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

      __lowerCamelCase     :      Dict       						=						WavLMModel(UpperCAmelCase      )

      recursively_load_weights(UpperCAmelCase						,	UpperCAmelCase      )

      hf_wavlm.save_pretrained(UpperCAmelCase      )


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



