
'''simple docstring'''
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_snake_case     						=						{
    '''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       _A	(			snake_case	,							snake_case	,							snake_case	,							snake_case	,							snake_case  )    ->			Tuple:







   for attribute in key.split("."  ):
      _lowercase    :   int      =     getattr(snake_case	,							snake_case  )

   if weight_type is not None:
      _lowercase    :   Optional[Any]      =     getattr(snake_case	,							snake_case  ).shape
   else:
      _lowercase    :   str      =     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":
      _lowercase    :   int      =     value
   elif weight_type == "weight_g":
      _lowercase    :   Union[str, Any]      =     value
   elif weight_type == "weight_v":
      _lowercase    :   str      =     value
   elif weight_type == "bias":
      _lowercase    :   Optional[int]      =     value
   else:
      _lowercase    :   int      =     value

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







def       _A	(			snake_case	,							snake_case  )    ->			Tuple:
   _lowercase    :   Dict      =     []
   _lowercase    :   Union[str, Any]      =     fairseq_model.state_dict()

   _lowercase    :   Optional[int]      =     hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowercase    :   Dict      =     False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case	,							snake_case	,							snake_case	,							snake_case	,							hf_model.config.feat_extract_norm == "group"	,							)
         _lowercase    :   str      =     True
      else:
         for key, mapped_key in MAPPING.items():
            _lowercase    :   List[str]      =     mapped_key

            if key in name:
               _lowercase    :   str      =     True
               if "*" in mapped_key:
                  _lowercase    :   Optional[int]      =     name.split(snake_case  )[0].split("."  )[-2]
                  _lowercase    :   Tuple      =     mapped_key.replace("*"	,							snake_case  )
               if "weight_g" in name:
                  _lowercase    :   Tuple      =     'weight_g'
               elif "weight_v" in name:
                  _lowercase    :   Union[str, Any]      =     'weight_v'
               elif "weight" in name:
                  _lowercase    :   Any      =     'weight'
               elif "bias" in name:
                  _lowercase    :   List[str]      =     'bias'
               else:
                  _lowercase    :   Optional[int]      =     None
               set_recursively(snake_case	,							snake_case	,							snake_case	,							snake_case	,							snake_case  )
            continue
      if not is_used:
         unused_weights.append(snake_case  )

   logger.warning(F'''Unused weights: {unused_weights}'''  )







def       _A	(			snake_case	,							snake_case	,							snake_case	,							snake_case	,							snake_case  )    ->			str:
   _lowercase    :   int      =     full_name.split("conv_layers."  )[-1]
   _lowercase    :   int      =     name.split("."  )
   _lowercase    :   Tuple      =     int(items[0]  )
   _lowercase    :   str      =     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.'''
         )
         _lowercase    :   Union[str, Any]      =     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.'''
         )
         _lowercase    :   List[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."
         )
         _lowercase    :   Dict      =     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.'''
         )
         _lowercase    :   Any      =     value
         logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
   else:
      unused_weights.append(snake_case  )







def       _A	(			snake_case  )    ->			int:
   _lowercase    :   List[str]      =     HubertConfig()
   _lowercase    :   Dict      =     model.config

   _lowercase    :   Union[str, Any]      =     fs_config.activation_dropout
   _lowercase    :   Any      =     False
   _lowercase    :   str      =     fs_config.attention_dropout
   _lowercase    :   List[str]      =     False
   _lowercase    :   Optional[Any]      =     eval(fs_config.extractor_conv_feature_layers  )
   _lowercase    :   Tuple      =     [x[0] for x in conv_layers]
   _lowercase    :   Tuple      =     [x[1] for x in conv_layers]
   _lowercase    :   Optional[Any]      =     [x[2] for x in conv_layers]
   _lowercase    :   Any      =     'gelu'
   _lowercase    :   Any      =     'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
   _lowercase    :   List[str]      =     False
   _lowercase    :   List[str]      =     0.0
   _lowercase    :   int      =     0.0
   _lowercase    :   List[str]      =     fs_config.activation_fn
   _lowercase    :   List[Any]      =     fs_config.dropout
   _lowercase    :   str      =     fs_config.encoder_embed_dim
   _lowercase    :   Optional[int]      =     0.02
   _lowercase    :   Union[str, Any]      =     fs_config.encoder_ffn_embed_dim
   _lowercase    :   Optional[int]      =     1E-5
   _lowercase    :   Tuple      =     0.0
   _lowercase    :   int      =     fs_config.encoder_attention_heads
   _lowercase    :   Tuple      =     fs_config.conv_pos_groups
   _lowercase    :   Optional[int]      =     fs_config.conv_pos
   _lowercase    :   Optional[Any]      =     len(snake_case  )
   _lowercase    :   int      =     fs_config.encoder_layers

   return config







@torch.no_grad()
def       _A	(			snake_case	,							snake_case=None  )    ->			Dict:
   _lowercase    :   Any      =     distilhubert().model.model

   if config_path is not None:
      _lowercase    :   Any      =     HubertConfig.from_pretrained(snake_case  )
   else:
      _lowercase    :   Union[str, Any]      =     convert_config(snake_case  )
   _lowercase    :   Dict      =     model.eval()

   _lowercase    :   Optional[Any]      =     WavaVecaFeatureExtractor(
       feature_size=1	,							sampling_rate=1_60_00	,							padding_value=0	,							do_normalize=snake_case	,							return_attention_mask=snake_case	,							)
   _lowercase    :   Tuple      =     HubertModel(snake_case  )

   recursively_load_weights(snake_case	,							snake_case  )

   feature_extractor.save_pretrained(snake_case  )
   hf_model.save_pretrained(snake_case  )


if __name__ == "__main__":
    _snake_case     						=						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')
    _snake_case     						=						parser.parse_args()
    convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)









'''simple docstring'''
import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


_snake_case     						=						logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf


@dataclass
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  int			 =							'tf'
   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Zone of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of Cloud TPU-enabled project'}						,   )

   _SCREAMING_SNAKE_CASE					:  float			 =							field(
       default=1.0						,   metadata={'help': 'Power for the Polynomial decay LR scheduler.'}						,   )

   _SCREAMING_SNAKE_CASE					:  bool			 =							field(default=lowerCamelCase_						,   metadata={'help': 'Whether to activate the XLA compilation or not'}						)


   @cached_property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      logger.info("Tensorflow: setting up strategy"						)

      _lowercase    :   Optional[int]      =     tf.config.list_physical_devices("GPU"						)

      # Set to float16 at first
      if self.fpaa:
         tf.keras.mixed_precision.set_global_policy("mixed_float16"						)

      if self.no_cuda:
         _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
      else:
         try:
            if self.tpu_name:
               _lowercase    :   int      =     tf.distribute.cluster_resolver.TPUClusterResolver(
                   self.tpu_name    ,       zone=self.tpu_zone    ,       project=self.gcp_project						)
            else:
               _lowercase    :   Any      =     tf.distribute.cluster_resolver.TPUClusterResolver()
         except ValueError:
            if self.tpu_name:
               raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''						)
            else:
               _lowercase    :   Tuple      =     None

         if tpu:
            # Set to bfloat16 in case of TPU
            if self.fpaa:
               tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"						)

            tf.config.experimental_connect_to_cluster(_UpperCamelCase						)
            tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase						)

            _lowercase    :   List[str]      =     tf.distribute.TPUStrategy(_UpperCamelCase						)

         elif len(_UpperCamelCase						) == 0:
            _lowercase    :   Optional[Any]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
         elif len(_UpperCamelCase						) == 1:
            _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/gpu:0"						)
         elif len(_UpperCamelCase						) > 1:
            # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
            _lowercase    :   Any      =     tf.distribute.MirroredStrategy()
         else:
            raise ValueError("Cannot find the proper strategy, please check your environment properties."						)

      return strategy


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      return self._setup_strategy


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      return self._setup_strategy.num_replicas_in_sync


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return False  # TF Logging is handled by Keras not the Trainer


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      if self.per_gpu_train_batch_size:
         logger.warning(
             "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
             "version. Using `--per_device_train_batch_size` is preferred."						)
      _lowercase    :   str      =     self.per_gpu_train_batch_size or self.per_device_train_batch_size
      return per_device_batch_size * self.n_replicas


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      if self.per_gpu_eval_batch_size:
         logger.warning(
             "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
             "version. Using `--per_device_eval_batch_size` is preferred."						)
      _lowercase    :   List[Any]      =     self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
      return per_device_batch_size * self.n_replicas






   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      requires_backends(self    ,       ["tf"]						)
      warnings.warn(
          "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."    ,       _UpperCamelCase    ,       )
      return self._setup_strategy.num_replicas_in_sync






