





"""simple docstring"""





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()
lowercase__				:Tuple				   =       logging.get_logger(__name__)

lowercase__				:Dict				   =       {
    '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_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


  for attribute in key.split('''.'''  ):
    __UpperCAmelCase    :						List[Any]       =						getattr(UpperCAmelCase_  ,			UpperCAmelCase_  )

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

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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						int       =						[]
  __UpperCAmelCase    :						Dict       =						fairseq_model.state_dict()

  __UpperCAmelCase    :						Any       =						hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    __UpperCAmelCase    :						Optional[int]       =						False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			hf_model.config.feat_extract_norm == '''group'''  ,			)
      __UpperCAmelCase    :						List[Any]       =						True
    else:
      for key, mapped_key in MAPPING.items():
        __UpperCAmelCase    :						Any       =						'''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):
          __UpperCAmelCase    :						int       =						True
          if "*" in mapped_key:
            __UpperCAmelCase    :						Optional[Any]       =						name.split(UpperCAmelCase_  )[0].split('''.'''  )[-2]
            __UpperCAmelCase    :						Optional[int]       =						mapped_key.replace('''*'''  ,			UpperCAmelCase_  )
          if "weight_g" in name:
            __UpperCAmelCase    :						Optional[Any]       =						'''weight_g'''
          elif "weight_v" in name:
            __UpperCAmelCase    :						Union[str, Any]       =						'''weight_v'''
          elif "weight" in name:
            __UpperCAmelCase    :						Optional[Any]       =						'''weight'''
          elif "bias" in name:
            __UpperCAmelCase    :						Any       =						'''bias'''
          else:
            __UpperCAmelCase    :						str       =						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_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						full_name.split('''conv_layers.'''  )[-1]
  __UpperCAmelCase    :						Optional[int]       =						name.split('''.'''  )
  __UpperCAmelCase    :						List[str]       =						int(items[0]  )
  __UpperCAmelCase    :						int       =						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    :						str       =						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    :						Union[str, 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    :						int       =						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    :						Optional[Any]       =						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_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=True  )						->Optional[Any]:





  """simple docstring"""


  if config_path is not None:
    __UpperCAmelCase    :						Optional[Any]       =						HubertConfig.from_pretrained(UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						Tuple       =						HubertConfig()

  if is_finetuned:
    if dict_path:
      __UpperCAmelCase    :						Optional[Any]       =						Dictionary.load(UpperCAmelCase_  )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      __UpperCAmelCase    :						int       =						target_dict.pad_index
      __UpperCAmelCase    :						Union[str, Any]       =						target_dict.bos_index
      __UpperCAmelCase    :						List[str]       =						target_dict.eos_index
      __UpperCAmelCase    :						str       =						len(target_dict.symbols  )
      __UpperCAmelCase    :						Dict       =						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_  )
      __UpperCAmelCase    :						str       =						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_  ,			)
      __UpperCAmelCase    :						Optional[int]       =						True if config.feat_extract_norm == '''layer''' else False
      __UpperCAmelCase    :						Dict       =						WavaVecaFeatureExtractor(
          feature_size=1  ,			sampling_rate=1_60_00  ,			padding_value=0  ,			do_normalize=UpperCAmelCase_  ,			return_attention_mask=UpperCAmelCase_  ,			)
      __UpperCAmelCase    :						List[Any]       =						WavaVecaProcessor(feature_extractor=UpperCAmelCase_  ,			tokenizer=UpperCAmelCase_  )
      processor.save_pretrained(UpperCAmelCase_  )

    __UpperCAmelCase    :						Union[str, Any]       =						HubertForCTC(UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						List[str]       =						HubertModel(UpperCAmelCase_  )

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

  __UpperCAmelCase    :						Any       =						model[0].eval()

  recursively_load_weights(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )

  hf_wavavec.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:int				   =       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'
  )
  lowercase__				:Tuple				   =       parser.parse_args()
  convert_hubert_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )






"""simple docstring"""





from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

lowercase__				:str				   =       (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase__				:list[int]				   =       [ord(letter) for letter in string.ascii_lowercase]
lowercase__				:set[int]				   =       {ord(char) for char in VALID_CHARS}

lowercase__				:list[str]				   =       ["the", "be", "to", "of", "and", "in", "that", "have"]


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->str | None:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						""
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int
  __UpperCAmelCase    :						int

  for keychar, cipherchar in zip(cycle(UpperCAmelCase_  )  ,			UpperCAmelCase_  ):
    __UpperCAmelCase    :						Optional[Any]       =						cipherchar ^ keychar
    if decodedchar not in VALID_INTS:
      return None
    decoded += chr(UpperCAmelCase_  )

  return decoded


def       lowerCamelCase_       (   UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  __UpperCAmelCase    :						list[str]       =						[]
  for key in product(UpperCAmelCase_  ,			repeat=3  ):
    __UpperCAmelCase    :						List[Any]       =						try_key(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if encoded is not None:
      possibles.append(UpperCAmelCase_  )
  return possibles


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->list[str]:





  """simple docstring"""


  return [possible for possible in possibles if common_word in possible.lower()]


def       lowerCamelCase_       (   UpperCAmelCase_ = "p059_cipher.txt"  )						->int:





  """simple docstring"""


  __UpperCAmelCase    :						list[int]
  __UpperCAmelCase    :						list[str]
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str
  __UpperCAmelCase    :						str       =						Path(UpperCAmelCase_  ).parent.joinpath(UpperCAmelCase_  ).read_text(encoding='''utf-8'''  )

  __UpperCAmelCase    :						List[str]       =						[int(UpperCAmelCase_  ) for number in data.strip().split(''','''  )]

  __UpperCAmelCase    :						List[Any]       =						filter_valid_chars(UpperCAmelCase_  )
  for common_word in COMMON_WORDS:
    __UpperCAmelCase    :						Optional[int]       =						filter_common_word(UpperCAmelCase_  ,			UpperCAmelCase_  )
    if len(UpperCAmelCase_  ) == 1:
      break

  __UpperCAmelCase    :						Optional[Any]       =						possibles[0]
  return sum(ord(UpperCAmelCase_  ) for char in decoded_text  )


if __name__ == "__main__":
  print(f"""{solution() = }""")