





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

a_    :						Tuple    		=      {
    "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",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  List[Any]	)		->							List[Any]:
 '''simple docstring'''


 for attribute in key.split('.'	):
  SCREAMING_SNAKE_CASE								=			getattr(_UpperCamelCase		,							_UpperCamelCase	)

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

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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Dict		,							_UpperCamelCase		:  List[Any]	)		->							Union[str, Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE								=			fairseq_model.state_dict()

 SCREAMING_SNAKE_CASE								=			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  SCREAMING_SNAKE_CASE								=			False
  if "conv_layers" in name:
   load_conv_layer(
       _UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							hf_model.config.feat_extract_norm == 'group'		,							)
   SCREAMING_SNAKE_CASE								=			True
  else:
   for key, mapped_key in MAPPING.items():
    SCREAMING_SNAKE_CASE								=			'hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

    if key in name or (key.split('w2v_model.'	)[-1] == name.split('.'	)[0] and not is_finetuned):
     SCREAMING_SNAKE_CASE								=			True
     if "*" in mapped_key:
      SCREAMING_SNAKE_CASE								=			name.split(_UpperCamelCase	)[0].split('.'	)[-2]
      SCREAMING_SNAKE_CASE								=			mapped_key.replace('*'		,							_UpperCamelCase	)
     if "weight_g" in name:
      SCREAMING_SNAKE_CASE								=			'weight_g'
     elif "weight_v" in name:
      SCREAMING_SNAKE_CASE								=			'weight_v'
     elif "weight" in name:
      SCREAMING_SNAKE_CASE								=			'weight'
     elif "bias" in name:
      SCREAMING_SNAKE_CASE								=			'bias'
     else:
      SCREAMING_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        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  str		,							_UpperCamelCase		:  Optional[Any]		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  int	)		->							Dict:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			full_name.split('conv_layers.'	)[-1]
 SCREAMING_SNAKE_CASE								=			name.split('.'	)
 SCREAMING_SNAKE_CASE								=			int(items[0]	)
 SCREAMING_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."""
   )
   SCREAMING_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."""
   )
   SCREAMING_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."
   )
   SCREAMING_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."""
   )
   SCREAMING_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	)


@torch.no_grad()
def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Union[str, Any]=None		,							_UpperCamelCase		:  Tuple=None		,							_UpperCamelCase		:  Any=True	)		->							Tuple:
 '''simple docstring'''


 if config_path is not None:
  SCREAMING_SNAKE_CASE								=			HubertConfig.from_pretrained(_UpperCamelCase	)
 else:
  SCREAMING_SNAKE_CASE								=			HubertConfig()

 if is_finetuned:
  if dict_path:
   SCREAMING_SNAKE_CASE								=			Dictionary.load(_UpperCamelCase	)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   SCREAMING_SNAKE_CASE								=			target_dict.pad_index
   SCREAMING_SNAKE_CASE								=			target_dict.bos_index
   SCREAMING_SNAKE_CASE								=			target_dict.eos_index
   SCREAMING_SNAKE_CASE								=			len(target_dict.symbols	)
   SCREAMING_SNAKE_CASE								=			os.path.join(_UpperCamelCase		,							'vocab.json'	)
   if not os.path.isdir(_UpperCamelCase	):
    logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_UpperCamelCase	)	)
    return
   os.makedirs(_UpperCamelCase		,							exist_ok=_UpperCamelCase	)
   with open(_UpperCamelCase		,							'w'		,							encoding='utf-8'	) as vocab_handle:
    json.dump(target_dict.indices		,							_UpperCamelCase	)
   SCREAMING_SNAKE_CASE								=			WavaVecaCTCTokenizer(
       _UpperCamelCase		,							unk_token=target_dict.unk_word		,							pad_token=target_dict.pad_word		,							bos_token=target_dict.bos_word		,							eos_token=target_dict.eos_word		,							word_delimiter_token='|'		,							do_lower_case=_UpperCamelCase		,							)
   SCREAMING_SNAKE_CASE								=			True if config.feat_extract_norm == 'layer' else False
   SCREAMING_SNAKE_CASE								=			WavaVecaFeatureExtractor(
       feature_size=1		,							sampling_rate=1_60_00		,							padding_value=0		,							do_normalize=_UpperCamelCase		,							return_attention_mask=_UpperCamelCase		,							)
   SCREAMING_SNAKE_CASE								=			WavaVecaProcessor(feature_extractor=_UpperCamelCase		,							tokenizer=_UpperCamelCase	)
   processor.save_pretrained(_UpperCamelCase	)

  SCREAMING_SNAKE_CASE								=			HubertForCTC(_UpperCamelCase	)
 else:
  SCREAMING_SNAKE_CASE								=			HubertModel(_UpperCamelCase	)

 if is_finetuned:
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]		,							arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
 else:
  SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

 SCREAMING_SNAKE_CASE								=			model[0].eval()

 recursively_load_weights(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase	)

 hf_wavavec.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
      a_    :						Tuple    		=      argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
      parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      parser.add_argument(
          "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
      )
      a_    :						Optional[Any]    		=      parser.parse_args()
      convert_hubert_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


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

a_    :						Optional[Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Any    		=      {
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

a_    :						Union[str, Any]    		=      {"allegro/herbert-base-cased": 514}
a_    :						List[Any]    		=      {}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_INIT_CONFIGURATION
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =HerbertTokenizer

    def __init__(			self :      Tuple							,    snake_case__ :      Optional[Any]=None							,    snake_case__ :      int=None							,    snake_case__ :      Optional[int]=None							,    snake_case__ :      str="<s>"							,    snake_case__ :      Tuple="<unk>"							,    snake_case__ :      List[str]="<pad>"							,    snake_case__ :      Tuple="<mask>"							,    snake_case__ :      Dict="</s>"							,    **snake_case__ :      List[str]							,    ):

     """simple docstring"""






     super().__init__(
         snake_case__							,    snake_case__							,    tokenizer_file=snake_case__							,    cls_token=snake_case__							,    unk_token=snake_case__							,    pad_token=snake_case__							,    mask_token=snake_case__							,    sep_token=snake_case__							,    **snake_case__							,    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.cls_token_id]
     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     if token_ids_a is None:
      return cls + token_ids_a + sep

     return cls + token_ids_a + sep + token_ids_a + sep

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None							,    snake_case__ :      bool = False    ):

     """simple docstring"""






     if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=snake_case__							,    token_ids_a=snake_case__							,    already_has_special_tokens=snake_case__    )

     if token_ids_a is None:
      return [1] + ([0] * len(snake_case__    )) + [1]
     return [1] + ([0] * len(snake_case__    )) + [1] + ([0] * len(snake_case__    )) + [1]

    def UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep    ) * [0]
     return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]




    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._tokenizer.model.save(snake_case__							,    name=snake_case__    )
     return tuple(snake_case__    )

