




'''simple docstring'''





import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_ = 4_096						):
    _a       , _a       , _a				:							Optional[int]							       =      key.shape[-3:]
    _a				:							Tuple							       =      value.shape[-1]
    _a				:							int							       =      min(UpperCAmelCase_   ,	UpperCAmelCase_						)
    _a				:							Tuple							       =      query / jnp.sqrt(UpperCAmelCase_						)

    @functools.partial(jax.checkpoint   ,	prevent_cse=UpperCAmelCase_						)
    def summarize_chunk(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						):
        _a				:							Dict							       =      jnp.einsum("""...qhd,...khd->...qhk"""   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	precision=UpperCAmelCase_						)

        _a				:							List[str]							       =      jnp.max(UpperCAmelCase_   ,	axis=-1   ,	keepdims=UpperCAmelCase_						)
        _a				:							Any							       =      jax.lax.stop_gradient(UpperCAmelCase_						)
        _a				:							Tuple							       =      jnp.exp(attn_weights - max_score						)

        _a				:							List[str]							       =      jnp.einsum("""...vhf,...qhv->...qhf"""   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	precision=UpperCAmelCase_						)
        _a				:							List[Any]							       =      jnp.einsum("""...qhk->...qh"""   ,	UpperCAmelCase_						)

        return (exp_values, exp_weights.sum(axis=-1						), max_score)

    def chunk_scanner(UpperCAmelCase_						):
        # julienne key array
        _a				:							Any							       =      jax.lax.dynamic_slice(
            operand=UpperCAmelCase_   ,	start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]   ,	slice_sizes=list(key.shape[:-3]						) + [key_chunk_size, num_heads, k_features]   ,	)

        # julienne value array
        _a				:							Optional[Any]							       =      jax.lax.dynamic_slice(
            operand=UpperCAmelCase_   ,	start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]   ,	slice_sizes=list(value.shape[:-3]						) + [key_chunk_size, num_heads, v_features]   ,	)

        return summarize_chunk(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)

    _a       , _a       , _a				:							Optional[int]							       =      jax.lax.map(f=UpperCAmelCase_   ,	xs=jnp.arange(0   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)						)

    _a				:							Optional[Any]							       =      jnp.max(UpperCAmelCase_   ,	axis=0   ,	keepdims=UpperCAmelCase_						)
    _a				:							List[str]							       =      jnp.exp(chunk_max - global_max						)

    chunk_values *= jnp.expand_dims(UpperCAmelCase_   ,	axis=-1						)
    chunk_weights *= max_diffs

    _a				:							Optional[Any]							       =      chunk_values.sum(axis=0						)
    _a				:							List[str]							       =      jnp.expand_dims(UpperCAmelCase_   ,	-1						).sum(axis=0						)

    return all_values / all_weights





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_=jax.lax.Precision.HIGHEST   ,	UpperCAmelCase_ = 1_024   ,	UpperCAmelCase_ = 4_096						):
    _a       , _a       , _a				:							int							       =      query.shape[-3:]

    def chunk_scanner(UpperCAmelCase_   ,	UpperCAmelCase_						):
        # julienne query array
        _a				:							Any							       =      jax.lax.dynamic_slice(
            operand=UpperCAmelCase_   ,	start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]   ,	slice_sizes=list(query.shape[:-3]						) + [min(UpperCAmelCase_   ,	UpperCAmelCase_						), num_heads, q_features]   ,	)

        return (
            chunk_idx + query_chunk_size,  # unused ignore it
            _query_chunk_attention(
                query=UpperCAmelCase_   ,	key=UpperCAmelCase_   ,	value=UpperCAmelCase_   ,	precision=UpperCAmelCase_   ,	key_chunk_size=UpperCAmelCase_						),
        )

    _a       , _a				:							List[str]							       =      jax.lax.scan(
        f=UpperCAmelCase_   ,	init=0   ,	xs=UpperCAmelCase_   ,	length=math.ceil(num_q / query_chunk_size						)						)

    return jnp.concatenate(UpperCAmelCase_   ,	axis=-3						)  # fuse the chunked result back




class      UpperCamelCase       (	nn.Module       ):
  a     :int
  a     :int          =					8
  a     :int          =					6_4
  a     :float          =					0.0
  a     :bool          =					False
  a     :jnp.dtype          =					jnp.floataa



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      self.dim_head * self.heads
      _a				:							Any							       =      self.dim_head**-0.5

      # Weights were exported with old names {to_q, to_k, to_v, to_out}
      _a				:							Any							       =      nn.Dense(__lowerCAmelCase		,     use_bias=__lowerCAmelCase		,     dtype=self.dtype		,     name="""to_q""" )
      _a				:							Tuple							       =      nn.Dense(__lowerCAmelCase		,     use_bias=__lowerCAmelCase		,     dtype=self.dtype		,     name="""to_k""" )
      _a				:							Dict							       =      nn.Dense(__lowerCAmelCase		,     use_bias=__lowerCAmelCase		,     dtype=self.dtype		,     name="""to_v""" )

      _a				:							Union[str, Any]							       =      nn.Dense(self.query_dim		,     dtype=self.dtype		,     name="""to_out_0""" )
      _a				:							List[str]							       =      nn.Dropout(rate=self.dropout )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a       , _a       , _a				:							List[str]							       =      tensor.shape
      _a				:							List[str]							       =      self.heads
      _a				:							List[Any]							       =      tensor.reshape(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     dim // head_size )
      _a				:							Optional[Any]							       =      jnp.transpose(__lowerCAmelCase		,     (0, 2, 1, 3) )
      _a				:							Optional[Any]							       =      tensor.reshape(batch_size * head_size		,     __lowerCAmelCase		,     dim // head_size )
      return tensor



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a       , _a       , _a				:							str							       =      tensor.shape
      _a				:							Optional[int]							       =      self.heads
      _a				:							int							       =      tensor.reshape(batch_size // head_size		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
      _a				:							List[Any]							       =      jnp.transpose(__lowerCAmelCase		,     (0, 2, 1, 3) )
      _a				:							Tuple							       =      tensor.reshape(batch_size // head_size		,     __lowerCAmelCase		,     dim * head_size )
      return tensor



  def __call__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=True ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      hidden_states if context is None else context

      _a				:							Dict							       =      self.query(__lowerCAmelCase )
      _a				:							str							       =      self.key(__lowerCAmelCase )
      _a				:							Dict							       =      self.value(__lowerCAmelCase )

      _a				:							str							       =      self.reshape_heads_to_batch_dim(__lowerCAmelCase )
      _a				:							str							       =      self.reshape_heads_to_batch_dim(__lowerCAmelCase )
      _a				:							Tuple							       =      self.reshape_heads_to_batch_dim(__lowerCAmelCase )

      if self.use_memory_efficient_attention:
          _a				:							List[str]							       =      query_states.transpose(1		,     0		,     2 )
          _a				:							Dict							       =      key_states.transpose(1		,     0		,     2 )
          _a				:							int							       =      value_states.transpose(1		,     0		,     2 )

          # this if statement create a chunk size for each layer of the unet
          # the chunk size is equal to the query_length dimension of the deepest layer of the unet

          _a				:							Dict							       =      query_states.shape[-3]
          if flatten_latent_dim % 64 == 0:
              _a				:							Union[str, Any]							       =      int(flatten_latent_dim / 64 )
          elif flatten_latent_dim % 16 == 0:
              _a				:							Dict							       =      int(flatten_latent_dim / 16 )
          elif flatten_latent_dim % 4 == 0:
              _a				:							List[Any]							       =      int(flatten_latent_dim / 4 )
          else:
              _a				:							Optional[int]							       =      int(__lowerCAmelCase )

          _a				:							List[str]							       =      jax_memory_efficient_attention(
              __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     query_chunk_size=__lowerCAmelCase		,     key_chunk_size=4096 * 4 )

          _a				:							Optional[Any]							       =      hidden_states.transpose(1		,     0		,     2 )
      else:
          # compute attentions
          _a				:							Dict							       =      jnp.einsum("""b i d, b j d->b i j"""		,     __lowerCAmelCase		,     __lowerCAmelCase )
          _a				:							Tuple							       =      attention_scores * self.scale
          _a				:							Union[str, Any]							       =      nn.softmax(__lowerCAmelCase		,     axis=2 )

          # attend to values
          _a				:							Optional[Any]							       =      jnp.einsum("""b i j, b j d -> b i d"""		,     __lowerCAmelCase		,     __lowerCAmelCase )

      _a				:							List[Any]							       =      self.reshape_batch_dim_to_heads(__lowerCAmelCase )
      _a				:							Optional[Any]							       =      self.proj_attn(__lowerCAmelCase )
      return self.dropout_layer(__lowerCAmelCase		,     deterministic=__lowerCAmelCase )






class      UpperCamelCase       (	nn.Module       ):
  a     :int
  a     :int
  a     :int
  a     :float          =					0.0
  a     :bool          =					False
  a     :jnp.dtype          =					jnp.floataa
  a     :bool          =					False



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      FlaxAttention(
          self.dim		,     self.n_heads		,     self.d_head		,     self.dropout		,     self.use_memory_efficient_attention		,     dtype=self.dtype )
      # cross attention
      _a				:							Union[str, Any]							       =      FlaxAttention(
          self.dim		,     self.n_heads		,     self.d_head		,     self.dropout		,     self.use_memory_efficient_attention		,     dtype=self.dtype )
      _a				:							List[str]							       =      FlaxFeedForward(dim=self.dim		,     dropout=self.dropout		,     dtype=self.dtype )
      _a				:							str							       =      nn.LayerNorm(epsilon=1e-5		,     dtype=self.dtype )
      _a				:							Dict							       =      nn.LayerNorm(epsilon=1e-5		,     dtype=self.dtype )
      _a				:							Optional[int]							       =      nn.LayerNorm(epsilon=1e-5		,     dtype=self.dtype )
      _a				:							int							       =      nn.Dropout(rate=self.dropout )



  def __call__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=True ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      hidden_states
      if self.only_cross_attention:
          _a				:							Dict							       =      self.attna(self.norma(__lowerCAmelCase )		,     __lowerCAmelCase		,     deterministic=__lowerCAmelCase )
      else:
          _a				:							Optional[Any]							       =      self.attna(self.norma(__lowerCAmelCase )		,     deterministic=__lowerCAmelCase )
      _a				:							int							       =      hidden_states + residual

      # cross attention
      _a				:							int							       =      hidden_states
      _a				:							Any							       =      self.attna(self.norma(__lowerCAmelCase )		,     __lowerCAmelCase		,     deterministic=__lowerCAmelCase )
      _a				:							List[Any]							       =      hidden_states + residual

      # feed forward
      _a				:							List[str]							       =      hidden_states
      _a				:							str							       =      self.ff(self.norma(__lowerCAmelCase )		,     deterministic=__lowerCAmelCase )
      _a				:							Tuple							       =      hidden_states + residual

      return self.dropout_layer(__lowerCAmelCase		,     deterministic=__lowerCAmelCase )






class      UpperCamelCase       (	nn.Module       ):
  a     :int
  a     :int
  a     :int
  a     :int          =					1
  a     :float          =					0.0
  a     :bool          =					False
  a     :bool          =					False
  a     :jnp.dtype          =					jnp.floataa
  a     :bool          =					False



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      nn.GroupNorm(num_groups=32		,     epsilon=1e-5 )

      _a				:							Any							       =      self.n_heads * self.d_head
      if self.use_linear_projection:
          _a				:							Dict							       =      nn.Dense(__lowerCAmelCase		,     dtype=self.dtype )
      else:
          _a				:							List[Any]							       =      nn.Conv(
              __lowerCAmelCase		,     kernel_size=(1, 1)		,     strides=(1, 1)		,     padding="""VALID"""		,     dtype=self.dtype		,     )

      _a				:							Union[str, Any]							       =      [
          FlaxBasicTransformerBlock(
              __lowerCAmelCase		,     self.n_heads		,     self.d_head		,     dropout=self.dropout		,     only_cross_attention=self.only_cross_attention		,     dtype=self.dtype		,     use_memory_efficient_attention=self.use_memory_efficient_attention		,     )
          for _ in range(self.depth )
      ]

      if self.use_linear_projection:
          _a				:							List[str]							       =      nn.Dense(__lowerCAmelCase		,     dtype=self.dtype )
      else:
          _a				:							int							       =      nn.Conv(
              __lowerCAmelCase		,     kernel_size=(1, 1)		,     strides=(1, 1)		,     padding="""VALID"""		,     dtype=self.dtype		,     )

      _a				:							Optional[Any]							       =      nn.Dropout(rate=self.dropout )



  def __call__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=True ):







      '''simple docstring'''


      _a       , _a       , _a       , _a				:							int							       =      hidden_states.shape
      _a				:							Dict							       =      hidden_states
      _a				:							Optional[int]							       =      self.norm(__lowerCAmelCase )
      if self.use_linear_projection:
          _a				:							Union[str, Any]							       =      hidden_states.reshape(__lowerCAmelCase		,     height * width		,     __lowerCAmelCase )
          _a				:							Any							       =      self.proj_in(__lowerCAmelCase )
      else:
          _a				:							Tuple							       =      self.proj_in(__lowerCAmelCase )
          _a				:							List[Any]							       =      hidden_states.reshape(__lowerCAmelCase		,     height * width		,     __lowerCAmelCase )

      for transformer_block in self.transformer_blocks:
          _a				:							Dict							       =      transformer_block(__lowerCAmelCase		,     __lowerCAmelCase		,     deterministic=__lowerCAmelCase )

      if self.use_linear_projection:
          _a				:							str							       =      self.proj_out(__lowerCAmelCase )
          _a				:							int							       =      hidden_states.reshape(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
      else:
          _a				:							Union[str, Any]							       =      hidden_states.reshape(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase )
          _a				:							Tuple							       =      self.proj_out(__lowerCAmelCase )

      _a				:							Union[str, Any]							       =      hidden_states + residual
      return self.dropout_layer(__lowerCAmelCase		,     deterministic=__lowerCAmelCase )






class      UpperCamelCase       (	nn.Module       ):
  a     :int
  a     :float          =					0.0
  a     :jnp.dtype          =					jnp.floataa



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							List[str]							       =      FlaxGEGLU(self.dim		,     self.dropout		,     self.dtype )
      _a				:							Any							       =      nn.Dense(self.dim		,     dtype=self.dtype )



  def __call__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=True ):







      '''simple docstring'''


      _a				:							int							       =      self.net_a(__lowerCAmelCase		,     deterministic=__lowerCAmelCase )
      _a				:							Tuple							       =      self.net_a(__lowerCAmelCase )
      return hidden_states






class      UpperCamelCase       (	nn.Module       ):
  a     :int
  a     :float          =					0.0
  a     :jnp.dtype          =					jnp.floataa



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      self.dim * 4
      _a				:							Optional[int]							       =      nn.Dense(inner_dim * 2		,     dtype=self.dtype )
      _a				:							List[Any]							       =      nn.Dropout(rate=self.dropout )



  def __call__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=True ):







      '''simple docstring'''


      _a				:							Dict							       =      self.proj(__lowerCAmelCase )
      _a       , _a				:							Union[str, Any]							       =      jnp.split(__lowerCAmelCase		,     2		,     axis=2 )
      return self.dropout_layer(hidden_linear * nn.gelu(__lowerCAmelCase )		,     deterministic=__lowerCAmelCase )








import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_   							=      logging.get_logger(__name__)




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Any    ,     A__			:   int      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForSequenceClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""projector.weight"""]
       __lowerCamelCase  =		downstream_dict["""projector.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.post_net.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Union[str, Any]    ,     A__			:   Any    ,     A__			:   List[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForAudioFrameClassification.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""model.linear.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.linear.bias"""]
       return model




def    lowerCamelCase__			(		A__			:   Optional[Any]    ,     A__			:   Dict    ,     A__			:   Optional[Any]      ):





       '''simple docstring'''



       __lowerCamelCase  =		WavLMForXVector.from_pretrained(A__    ,     config=A__      )
       __lowerCamelCase  =		downstream_dict["""connector.weight"""]
       __lowerCamelCase  =		downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
              __lowerCamelCase  =		downstream_dict[
                  f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
              ]
              __lowerCamelCase  =		downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       __lowerCamelCase  =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       __lowerCamelCase  =		downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def    lowerCamelCase__			(		A__			:   Any    ,     A__			:   Tuple    ,     A__			:   int    ,     A__			:   str      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.load(A__    ,     map_location="""cpu"""      )

       __lowerCamelCase  =		checkpoint["""Downstream"""]

       __lowerCamelCase  =		WavLMConfig.from_pretrained(A__      )
       __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
           A__    ,     return_attention_mask=A__    ,     do_normalize=A__      )

       __lowerCamelCase  =		hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""      ):
              __lowerCamelCase  =		convert_classification(A__    ,     A__    ,     A__      )
       elif arch.endswith("""ForAudioFrameClassification"""      ):
              __lowerCamelCase  =		convert_diarization(A__    ,     A__    ,     A__      )
       elif arch.endswith("""ForXVector"""      ):
              __lowerCamelCase  =		convert_xvector(A__    ,     A__    ,     A__      )
       else:
              raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'      )

       if hf_config.use_weighted_layer_sum:
              __lowerCamelCase  =		checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(A__      )
       hf_model.save_pretrained(A__      )


if __name__ == "__main__":
       UpperCAmelCase_   							=      argparse.ArgumentParser()
       parser.add_argument(
           '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
       )
       parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
       parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
       UpperCAmelCase_   							=      parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

