

'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a_  :    Any      =			logging.get_logger(__name__)

a_  :    int      =			{
    '''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_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''',
    '''mask_emb''': '''masked_spec_embed''',
}





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						List[Any] ,    UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						str		):
 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 __snake_case (				UpperCAmelCase_						:						List[str] ,    UpperCAmelCase_						:						Union[str, Any]		):
 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():
    lowerCamelCase_								=  mapped_key

    if key in name:
     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 "weight" in name:
      lowerCamelCase_								=  'weight'
     elif "bias" in name:
      lowerCamelCase_								=  'bias'
     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 __snake_case (				UpperCAmelCase_						:						List[str] ,    UpperCAmelCase_						:						Optional[Any] ,    UpperCAmelCase_						:						int ,    UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						List[Any]		):
 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__		)





def __snake_case (				UpperCAmelCase_						:						List[Any]		):
 lowerCamelCase_								=  HubertConfig()
 lowerCamelCase_								=  model.config

 lowerCamelCase_								=  fs_config.activation_dropout
 lowerCamelCase_								=  False
 lowerCamelCase_								=  fs_config.attention_dropout
 lowerCamelCase_								=  False
 lowerCamelCase_								=  eval(fs_config.extractor_conv_feature_layers		)
 lowerCamelCase_								=  [x[0] for x in conv_layers]
 lowerCamelCase_								=  [x[1] for x in conv_layers]
 lowerCamelCase_								=  [x[2] for x in conv_layers]
 lowerCamelCase_								=  'gelu'
 lowerCamelCase_								=  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 lowerCamelCase_								=  False
 lowerCamelCase_								=  0.0
 lowerCamelCase_								=  0.0
 lowerCamelCase_								=  fs_config.activation_fn
 lowerCamelCase_								=  fs_config.dropout
 lowerCamelCase_								=  fs_config.encoder_embed_dim
 lowerCamelCase_								=  0.02
 lowerCamelCase_								=  fs_config.encoder_ffn_embed_dim
 lowerCamelCase_								=  1E-5
 lowerCamelCase_								=  0.0
 lowerCamelCase_								=  fs_config.encoder_attention_heads
 lowerCamelCase_								=  fs_config.conv_pos_groups
 lowerCamelCase_								=  fs_config.conv_pos
 lowerCamelCase_								=  len(A__		)
 lowerCamelCase_								=  fs_config.encoder_layers

 return config





@torch.no_grad()
def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						Optional[int]=None		):
 lowerCamelCase_								=  distilhubert().model.model

 if config_path is not None:
  lowerCamelCase_								=  HubertConfig.from_pretrained(A__		)
 else:
  lowerCamelCase_								=  convert_config(A__		)
 lowerCamelCase_								=  model.eval()

 lowerCamelCase_								=  WavaVecaFeatureExtractor(
     feature_size=1 ,    sampling_rate=16000 ,    padding_value=0 ,    do_normalize=A__ ,    return_attention_mask=A__ ,    )
 lowerCamelCase_								=  HubertModel(A__		)

 recursively_load_weights(A__ ,    A__		)

 feature_extractor.save_pretrained(A__		)
 hf_model.save_pretrained(A__		)


if __name__ == "__main__":
     a_  :    Union[str, Any]      =			argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     a_  :    List[Any]      =			parser.parse_args()
     convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''

import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
     import tensorflow as tf

     from transformers.activations_tf import get_tf_activation





@require_tf
class 					snake_case   (   unittest.TestCase      ):





  """simple docstring"""




  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]					)
   lowerCamelCase_								=  get_tf_activation("gelu"					)
   lowerCamelCase_								=  get_tf_activation("gelu_10"					)

   lowerCamelCase_								=  gelu(UpperCamelCase					)
   lowerCamelCase_								=  geluaa(UpperCamelCase					)

   lowerCamelCase_								=  tf.where(y_gelu_aa < 10.0      ,  1.0      ,  0.0					)

   self.assertEqual(tf.math.reduce_max(UpperCamelCase					).numpy().item()      ,  10.0					)
   self.assertTrue(np.allclose(y_gelu * clipped_mask      ,  y_gelu_aa * clipped_mask					)					)







  def   snake_case		(   self					):







   """simple docstring"""
   get_tf_activation("gelu"					)
   get_tf_activation("gelu_10"					)
   get_tf_activation("gelu_fast"					)
   get_tf_activation("gelu_new"					)
   get_tf_activation("glu"					)
   get_tf_activation("mish"					)
   get_tf_activation("quick_gelu"					)
   get_tf_activation("relu"					)
   get_tf_activation("sigmoid"					)
   get_tf_activation("silu"					)
   get_tf_activation("swish"					)
   get_tf_activation("tanh"					)
   with self.assertRaises(UpperCamelCase					):
    get_tf_activation("bogus"					)
   with self.assertRaises(UpperCamelCase					):
    get_tf_activation(UpperCamelCase					)



