
'''simple docstring'''





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

SCREAMING_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 snake_case_    (				lowercase__     :   Any      ,					lowercase__     :   Tuple      ,					lowercase__     :   Optional[int]      ,					lowercase__     :   List[Any]      ,					lowercase__     :   Union[str, Any]		):







  for attribute in key.split("."		):
    UpperCAmelCase__			:				List[str]												=       getattr(_A      ,					_A		)

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

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







def snake_case_    (				lowercase__     :   Dict      ,					lowercase__     :   Optional[int]		):
  UpperCAmelCase__			:				Tuple												=       []
  UpperCAmelCase__			:				int												=       fairseq_model.state_dict()

  UpperCAmelCase__			:				Any												=       hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    UpperCAmelCase__			:				Any												=       False
    if "conv_layers" in name:
      load_conv_layer(
          _A      ,					_A      ,					_A      ,					_A      ,					hf_model.config.feat_extract_norm == "group"      ,					)
      UpperCAmelCase__			:				List[Any]												=       True
    else:
      for key, mapped_key in MAPPING.items():
        UpperCAmelCase__			:				int												=       mapped_key

        if key in name:
          UpperCAmelCase__			:				Tuple												=       True
          if "*" in mapped_key:
            UpperCAmelCase__			:				Optional[Any]												=       name.split(_A		)[0].split("."		)[-2]
            UpperCAmelCase__			:				Any												=       mapped_key.replace("*"      ,					_A		)
          if "weight_g" in name:
            UpperCAmelCase__			:				Any												=       "weight_g"
          elif "weight_v" in name:
            UpperCAmelCase__			:				int												=       "weight_v"
          elif "weight" in name:
            UpperCAmelCase__			:				Optional[int]												=       "weight"
          elif "bias" in name:
            UpperCAmelCase__			:				int												=       "bias"
          else:
            UpperCAmelCase__			:				Optional[Any]												=       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_    (				lowercase__     :   Optional[int]      ,					lowercase__     :   str      ,					lowercase__     :   Optional[Any]      ,					lowercase__     :   Dict      ,					lowercase__     :   List[Any]		):
  UpperCAmelCase__			:				Tuple												=       full_name.split("conv_layers."		)[-1]
  UpperCAmelCase__			:				Union[str, Any]												=       name.split("."		)
  UpperCAmelCase__			:				Dict												=       int(items[0]		)
  UpperCAmelCase__			:				Dict												=       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."""
      )
      UpperCAmelCase__			:				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."""
      )
      UpperCAmelCase__			:				Optional[Any]												=       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."
      )
      UpperCAmelCase__			:				List[str]												=       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."""
      )
      UpperCAmelCase__			:				Any												=       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_    (				lowercase__     :   str		):
  UpperCAmelCase__			:				List[Any]												=       HubertConfig()
  UpperCAmelCase__			:				Optional[Any]												=       model.config

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

  return config







@torch.no_grad()
def snake_case_    (				lowercase__     :   Tuple      ,					lowercase__     :   List[Any]=None		):
  UpperCAmelCase__			:				str												=       distilhubert().model.model

  if config_path is not None:
    UpperCAmelCase__			:				Any												=       HubertConfig.from_pretrained(_A		)
  else:
    UpperCAmelCase__			:				List[Any]												=       convert_config(_A		)
  UpperCAmelCase__			:				List[str]												=       model.eval()

  UpperCAmelCase__			:				int												=       WavaVecaFeatureExtractor(
      feature_size=1      ,					sampling_rate=1_6_0_0_0      ,					padding_value=0      ,					do_normalize=_A      ,					return_attention_mask=_A      ,					)
  UpperCAmelCase__			:				Optional[Any]												=       HubertModel(_A		)

  recursively_load_weights(_A      ,					_A		)

  feature_extractor.save_pretrained(_A		)
  hf_model.save_pretrained(_A		)


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





'''simple docstring'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


SCREAMING_SNAKE_CASE    =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =					{
    """gpt2""": """https://huggingface.co/gpt2/resolve/main/config.json""",
    """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/config.json""",
    """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/config.json""",
    """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/config.json""",
    """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/config.json""",
}






class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						Optional[int]				=       "gpt2"
 lowercase_				:						int				=       ["past_key_values"]
 lowercase_				:						Any				=       {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }







 def __init__(				self		:     Union[str, Any] ,							snake_case__		:     Optional[int]=5_02_57 ,							snake_case__		:     List[Any]=10_24 ,							snake_case__		:     Tuple=7_68 ,							snake_case__		:     List[Any]=12 ,							snake_case__		:     List[str]=12 ,							snake_case__		:     Union[str, Any]=None ,							snake_case__		:     str="gelu_new" ,							snake_case__		:     Any=0.1 ,							snake_case__		:     Union[str, Any]=0.1 ,							snake_case__		:     Tuple=0.1 ,							snake_case__		:     List[str]=1e-5 ,							snake_case__		:     Optional[int]=0.02 ,							snake_case__		:     int="cls_index" ,							snake_case__		:     Tuple=True ,							snake_case__		:     List[Any]=None ,							snake_case__		:     Optional[int]=True ,							snake_case__		:     Optional[Any]=0.1 ,							snake_case__		:     Tuple=True ,							snake_case__		:     int=True ,							snake_case__		:     Optional[int]=5_02_56 ,							snake_case__		:     List[Any]=5_02_56 ,							snake_case__		:     Union[str, Any]=False ,							snake_case__		:     Optional[int]=False ,							**snake_case__		:     List[Any] ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				List[str]												=       vocab_size
   UpperCAmelCase__			:				Any												=       n_positions
   UpperCAmelCase__			:				str												=       n_embd
   UpperCAmelCase__			:				List[Any]												=       n_layer
   UpperCAmelCase__			:				Any												=       n_head
   UpperCAmelCase__			:				Any												=       n_inner
   UpperCAmelCase__			:				Tuple												=       activation_function
   UpperCAmelCase__			:				List[str]												=       resid_pdrop
   UpperCAmelCase__			:				Union[str, Any]												=       embd_pdrop
   UpperCAmelCase__			:				int												=       attn_pdrop
   UpperCAmelCase__			:				Optional[Any]												=       layer_norm_epsilon
   UpperCAmelCase__			:				Optional[Any]												=       initializer_range
   UpperCAmelCase__			:				Optional[Any]												=       summary_type
   UpperCAmelCase__			:				Tuple												=       summary_use_proj
   UpperCAmelCase__			:				int												=       summary_activation
   UpperCAmelCase__			:				List[str]												=       summary_first_dropout
   UpperCAmelCase__			:				Optional[Any]												=       summary_proj_to_labels
   UpperCAmelCase__			:				Optional[Any]												=       scale_attn_weights
   UpperCAmelCase__			:				List[Any]												=       use_cache
   UpperCAmelCase__			:				Union[str, Any]												=       scale_attn_by_inverse_layer_idx
   UpperCAmelCase__			:				Tuple												=       reorder_and_upcast_attn

   UpperCAmelCase__			:				Dict												=       bos_token_id
   UpperCAmelCase__			:				Union[str, Any]												=       eos_token_id

   super().__init__(bos_token_id=snake_case__ ,							eos_token_id=snake_case__ ,							**snake_case__   )






class        UpperCAmelCase_       (						A							):

 '''simple docstring'''







 def __init__(				self		:     Union[str, Any] ,							snake_case__		:     PretrainedConfig ,							snake_case__		:     str = "default" ,							snake_case__		:     List[PatchingSpec] = None ,							snake_case__		:     bool = False ,							):



   '''simple docstring'''




   super().__init__(snake_case__ ,							task=snake_case__ ,							patching_specs=snake_case__ ,							use_past=snake_case__   )
   if not getattr(self._config ,							"pad_token_id" ,							snake_case__   ):
     # TODO: how to do that better?
     UpperCAmelCase__			:				List[str]												=       0







 @property
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}   )
   if self.use_past:
     self.fill_with_past_key_values_(snake_case__ ,							direction="inputs"   )
     UpperCAmelCase__			:				Dict												=       {0: "batch", 1: "past_sequence + sequence"}
   else:
     UpperCAmelCase__			:				Dict												=       {0: "batch", 1: "sequence"}

   return common_inputs







 @property
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   return self._config.n_layer







 @property
 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   return self._config.n_head







 def        UpperCamelCase     (				self		:     int ,							snake_case__		:     PreTrainedTokenizer ,							snake_case__		:     int = -1 ,							snake_case__		:     int = -1 ,							snake_case__		:     bool = False ,							snake_case__		:     Optional[TensorType] = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       super(snake_case__ ,							self   ).generate_dummy_inputs(
       snake_case__ ,							batch_size=snake_case__ ,							seq_length=snake_case__ ,							is_pair=snake_case__ ,							framework=snake_case__   )

   # We need to order the input in the way they appears in the forward()
   UpperCAmelCase__			:				Dict												=       OrderedDict({"input_ids": common_inputs["input_ids"]}   )

   # Need to add the past_keys
   if self.use_past:
     if not is_torch_available():
       raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
     else:
       import torch

       UpperCAmelCase__					, UpperCAmelCase__			:				int												=       common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       UpperCAmelCase__			:				int												=       seqlen + 2
       UpperCAmelCase__			:				Union[str, Any]												=       (
           batch,
           self.num_attention_heads,
           past_key_values_length,
           self._config.hidden_size // self.num_attention_heads,
       )
       UpperCAmelCase__			:				Optional[Any]												=       [
           (torch.zeros(snake_case__   ), torch.zeros(snake_case__   )) for _ in range(self.num_layers   )
       ]

   UpperCAmelCase__			:				List[str]												=       common_inputs["attention_mask"]
   if self.use_past:
     UpperCAmelCase__			:				List[Any]												=       ordered_inputs["attention_mask"].dtype
     UpperCAmelCase__			:				Optional[int]												=       torch.cat(
         [ordered_inputs["attention_mask"], torch.ones(snake_case__ ,							snake_case__ ,							dtype=snake_case__   )] ,							dim=1   )

   return ordered_inputs







 @property
 def        UpperCamelCase     (				self		:     Union[str, Any]   ):



   '''simple docstring'''




   return 13



