





"""simple docstring"""


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a__   :       List[Any]   						=			logging.get_logger(__name__)

a__   :       Optional[Any]   						=			{
    """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__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					int:

 '''simple docstring'''






 for attribute in key.split("."		):
  snake_case__      							=			getattr(_UpperCamelCase		,       _UpperCamelCase		)

 if weight_type is not None:
  snake_case__      							=			getattr(_UpperCamelCase		,       _UpperCamelCase		).shape
 else:
  snake_case__      							=			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":
  snake_case__      							=			value
 elif weight_type == "weight_g":
  snake_case__      							=			value
 elif weight_type == "weight_v":
  snake_case__      							=			value
 elif weight_type == "bias":
  snake_case__      							=			value
 else:
  snake_case__      							=			value

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





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					Optional[int]:

 '''simple docstring'''






 snake_case__      							=			[]
 snake_case__      							=			fairseq_model.state_dict()

 snake_case__      							=			hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  snake_case__      							=			False
  if "conv_layers" in name:
   load_conv_layer(
       _UpperCamelCase		,       _UpperCamelCase		,       _UpperCamelCase		,       _UpperCamelCase		,       hf_model.config.feat_extract_norm == "group"		,       )
   snake_case__      							=			True
  else:
   for key, mapped_key in MAPPING.items():
    snake_case__      							=			mapped_key

    if key in name:
     snake_case__      							=			True
     if "*" in mapped_key:
      snake_case__      							=			name.split(_UpperCamelCase		)[0].split("."		)[-2]
      snake_case__      							=			mapped_key.replace("*"		,       _UpperCamelCase		)
     if "weight_g" in name:
      snake_case__      							=			"weight_g"
     elif "weight_v" in name:
      snake_case__      							=			"weight_v"
     elif "weight" in name:
      snake_case__      							=			"weight"
     elif "bias" in name:
      snake_case__      							=			"bias"
     else:
      snake_case__      							=			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 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					Any:

 '''simple docstring'''






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





def 							a__     (					_UpperCAmelCase		)      ->					str:

 '''simple docstring'''






 snake_case__      							=			HubertConfig()
 snake_case__      							=			model.config

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

 return config





@torch.no_grad()
def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase=None		)      ->					List[str]:

 '''simple docstring'''






 snake_case__      							=			distilhubert().model.model

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

 snake_case__      							=			WavaVecaFeatureExtractor(
     feature_size=1		,       sampling_rate=1_6_0_0_0		,       padding_value=0		,       do_normalize=_UpperCamelCase		,       return_attention_mask=_UpperCamelCase		,       )
 snake_case__      							=			HubertModel(_UpperCamelCase		)

 recursively_load_weights(_UpperCamelCase		,       _UpperCamelCase		)

 feature_extractor.save_pretrained(_UpperCamelCase		)
 hf_model.save_pretrained(_UpperCamelCase		)


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 copy

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


a__   :       Optional[Any]   						=			logging.get_logger(__name__)

a__   :       List[str]   						=			{}



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							List[str]										=       """fsmt_decoder"""

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=0				,   __SCREAMING_SNAKE_CASE=0       ):






        '''simple docstring'''

        super().__init__()
        snake_case__      							=			vocab_size
        snake_case__      							=			bos_token_id



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							str										=       """fsmt"""
       snake_case__			:							List[Any]										=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

       def __init__(     self				,   __SCREAMING_SNAKE_CASE=["en", "de"]				,   __SCREAMING_SNAKE_CASE=4_2024				,   __SCREAMING_SNAKE_CASE=4_2024				,   __SCREAMING_SNAKE_CASE="relu"				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=200				,   __SCREAMING_SNAKE_CASE=1024				,   __SCREAMING_SNAKE_CASE=4096				,   __SCREAMING_SNAKE_CASE=12				,   __SCREAMING_SNAKE_CASE=16				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=4096				,   __SCREAMING_SNAKE_CASE=12				,   __SCREAMING_SNAKE_CASE=16				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.1				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=0.02				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=5				,   __SCREAMING_SNAKE_CASE=1.0				,   __SCREAMING_SNAKE_CASE=False				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=1				,   __SCREAMING_SNAKE_CASE=0				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=2				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__      							=			langs
        snake_case__      							=			src_vocab_size
        snake_case__      							=			tgt_vocab_size
        snake_case__      							=			d_model  # encoder_embed_dim and decoder_embed_dim

        snake_case__      							=			encoder_ffn_dim
        snake_case__      							=			snake_case__      							=			encoder_layers
        snake_case__      							=			encoder_attention_heads
        snake_case__      							=			encoder_layerdrop
        snake_case__      							=			decoder_layerdrop
        snake_case__      							=			decoder_ffn_dim
        snake_case__      							=			decoder_layers
        snake_case__      							=			decoder_attention_heads
        snake_case__      							=			max_position_embeddings
        snake_case__      							=			init_std  # Normal(0, this parameter)
        snake_case__      							=			activation_function

        snake_case__      							=			DecoderConfig(vocab_size=__SCREAMING_SNAKE_CASE				,   bos_token_id=__SCREAMING_SNAKE_CASE       )
        if "decoder" in common_kwargs:
         del common_kwargs["decoder"]

        snake_case__      							=			scale_embedding  # scale factor will be sqrt(d_model) if True

        # 3 Types of Dropout
        snake_case__      							=			attention_dropout
        snake_case__      							=			activation_dropout
        snake_case__      							=			dropout

        snake_case__      							=			use_cache
        super().__init__(
            pad_token_id=__SCREAMING_SNAKE_CASE				,   bos_token_id=__SCREAMING_SNAKE_CASE				,   eos_token_id=__SCREAMING_SNAKE_CASE				,   decoder_start_token_id=__SCREAMING_SNAKE_CASE				,   is_encoder_decoder=__SCREAMING_SNAKE_CASE				,   tie_word_embeddings=__SCREAMING_SNAKE_CASE				,   forced_eos_token_id=__SCREAMING_SNAKE_CASE				,   max_length=__SCREAMING_SNAKE_CASE				,   num_beams=__SCREAMING_SNAKE_CASE				,   length_penalty=__SCREAMING_SNAKE_CASE				,   early_stopping=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE				,   )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			copy.deepcopy(self.__dict__       )
        snake_case__      							=			self.decoder.to_dict()
        snake_case__      							=			self.__class__.model_type
        return output

