




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase__    :							str       		=     {
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		Optional[int]:





  """simple docstring"""





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

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

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


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			Optional[Any]      =     []
  _lowercase:			int      =     fairseq_model.state_dict()

  _lowercase:			List[Any]      =     hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _lowercase:			List[Any]      =     False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  hf_model.config.feat_extract_norm == 'group'       ,  )
      _lowercase:			int      =     True
    else:
      for key, mapped_key in MAPPING.items():
        _lowercase:			Optional[Any]      =     'sew_d.' + 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]:
          _lowercase:			List[Any]      =     True
          if "*" in mapped_key:
            _lowercase:			Optional[int]      =     name.split(UpperCamelCase				)[0].split('.'				)[-2]
            if not layer_index.isnumeric():
              continue
            _lowercase:			Optional[Any]      =     mapped_key.replace('*'       ,  UpperCamelCase				)
          if "weight_g" in name:
            _lowercase:			int      =     'weight_g'
          elif "weight_v" in name:
            _lowercase:			str      =     'weight_v'
          elif "weight" in name:
            _lowercase:			Union[str, Any]      =     'weight'
          elif "bias" in name:
            _lowercase:			List[Any]      =     'bias'
          else:
            _lowercase:			List[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				)      ->		Union[str, Any]:





  """simple docstring"""





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


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		List[Any]:





  """simple docstring"""





  _lowercase:			List[Any]      =     SEWDConfig()
  if is_finetuned:
    _lowercase:			Any      =     model.wav_encoder.wav_model.cfg
  else:
    _lowercase:			List[str]      =     model.cfg

  _lowercase:			List[Any]      =     fs_config.conv_bias
  _lowercase:			Dict      =     eval(fs_config.conv_feature_layers				)
  _lowercase:			str      =     [x[0] for x in conv_layers]
  _lowercase:			int      =     [x[1] for x in conv_layers]
  _lowercase:			List[Any]      =     [x[2] for x in conv_layers]
  _lowercase:			str      =     'gelu'
  _lowercase:			Optional[Any]      =     'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  _lowercase:			int      =     0.0
  _lowercase:			int      =     fs_config.activation_fn.name
  _lowercase:			Optional[int]      =     fs_config.encoder_embed_dim
  _lowercase:			Tuple      =     0.02
  _lowercase:			int      =     fs_config.encoder_ffn_embed_dim
  _lowercase:			str      =     1E-5
  _lowercase:			List[Any]      =     fs_config.encoder_layerdrop
  _lowercase:			Union[str, Any]      =     fs_config.encoder_attention_heads
  _lowercase:			int      =     fs_config.conv_pos_groups
  _lowercase:			Optional[int]      =     fs_config.conv_pos
  _lowercase:			Optional[Any]      =     len(UpperCamelCase				)
  _lowercase:			List[Any]      =     fs_config.encoder_layers
  _lowercase:			Tuple      =     fs_config.squeeze_factor
  # DeBERTa-specific parameters:
  _lowercase:			Optional[Any]      =     fs_config.max_position_embeddings
  _lowercase:			str      =     fs_config.position_buckets
  _lowercase:			List[Any]      =     fs_config.share_att_key
  _lowercase:			Any      =     fs_config.relative_attention
  _lowercase:			int      =     fs_config.position_biased_input
  _lowercase:			Optional[Any]      =     tuple(fs_config.pos_att_type.split('|'				)				)
  _lowercase:			Tuple      =     fs_config.norm_rel_ebd

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    _lowercase:			List[Any]      =     model.cfg
    _lowercase:			List[Any]      =     fs_config.final_dropout
    _lowercase:			Optional[Any]      =     fs_config.layerdrop
  _lowercase:			Tuple      =     fs_config.activation_dropout
  _lowercase:			int      =     fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  _lowercase:			Optional[Any]      =     fs_config.attention_dropout
  _lowercase:			str      =     fs_config.dropout_input
  _lowercase:			Optional[int]      =     fs_config.dropout
  _lowercase:			Optional[Any]      =     fs_config.mask_channel_length
  _lowercase:			List[str]      =     fs_config.mask_channel_prob
  _lowercase:			Any      =     fs_config.mask_length
  _lowercase:			Optional[int]      =     fs_config.mask_prob

  _lowercase:			List[str]      =     'Wav2Vec2FeatureExtractor'
  _lowercase:			Union[str, Any]      =     'Wav2Vec2CTCTokenizer'

  return config


@torch.no_grad()
def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase=None       ,  UpperCamelCase=None       ,  UpperCamelCase=True				)      ->		List[Any]:





  """simple docstring"""





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

  if config_path is not None:
    _lowercase:			List[str]      =     SEWDConfig.from_pretrained(UpperCamelCase				)
  else:
    _lowercase:			int      =     convert_config(model[0]       ,  UpperCamelCase				)
  _lowercase:			Dict      =     model[0].eval()

  _lowercase:			Optional[Any]      =     True if config.feat_extract_norm == 'layer' else False
  _lowercase:			List[Any]      =     WavaVecaFeatureExtractor(
      feature_size=1       ,  sampling_rate=16_000       ,  padding_value=0       ,  do_normalize=UpperCamelCase       ,  return_attention_mask=UpperCamelCase       ,  )

  if is_finetuned:
    if dict_path:
      _lowercase:			List[str]      =     Dictionary.load(UpperCamelCase				)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _lowercase:			Optional[int]      =     target_dict.pad_index
      _lowercase:			List[str]      =     target_dict.bos_index
      _lowercase:			List[str]      =     target_dict.pad_index
      _lowercase:			Tuple      =     target_dict.bos_index
      _lowercase:			Optional[Any]      =     target_dict.eos_index
      _lowercase:			Union[str, Any]      =     len(target_dict.symbols				)
      _lowercase:			List[str]      =     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				)
      _lowercase:			Tuple      =     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       ,  )
      _lowercase:			Optional[Any]      =     WavaVecaProcessor(feature_extractor=UpperCamelCase       ,  tokenizer=UpperCamelCase				)
      processor.save_pretrained(UpperCamelCase				)

    _lowercase:			Tuple      =     SEWDForCTC(UpperCamelCase				)
  else:
    _lowercase:			Tuple      =     SEWDModel(UpperCamelCase				)
    feature_extractor.save_pretrained(UpperCamelCase				)

  recursively_load_weights(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

  hf_model.save_pretrained(UpperCamelCase				)


if __name__ == "__main__":
   UpperCAmelCase__    :							Any       		=     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(
       '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
   )
   UpperCAmelCase__    :							Optional[int]       		=     parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )






import random
import sys

UpperCAmelCase__    :							str       		=     'ABCDEFGHIJKLMNOPQRSTUVWXYZ'


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  _lowercase:			Any      =     input('Enter message: '				)
  _lowercase:			List[str]      =     'LFWOAYUISVKMNXPBDCRJTQEGHZ'
  _lowercase:			Tuple      =     input('Encrypt/Decrypt [e/d]: '				)

  check_valid_key(UpperCamelCase				)

  if resp.lower().startswith('e'				):
    _lowercase:			int      =     'encrypt'
    _lowercase:			Optional[int]      =     encrypt_message(UpperCamelCase       ,  UpperCamelCase				)
  elif resp.lower().startswith('d'				):
    _lowercase:			str      =     'decrypt'
    _lowercase:			Dict      =     decrypt_message(UpperCamelCase       ,  UpperCamelCase				)

  print(F'\n{mode.title()}ion: \n{translated}'				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		None:





  """simple docstring"""





  _lowercase:			List[Any]      =     list(UpperCamelCase				)
  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  key_list.sort()
  letters_list.sort()

  if key_list != letters_list:
    sys.exit('Error in the key or symbol set.'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'encrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  return translate_message(UpperCamelCase       ,  UpperCamelCase       ,  'decrypt'				)


def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)      ->		str:





  """simple docstring"""





  _lowercase:			Optional[int]      =     ''
  _lowercase:			Any      =     LETTERS
  _lowercase:			Tuple      =     key

  if mode == "decrypt":
    _lowercase  ,       _lowercase:			Tuple      =     chars_b, chars_a

  for symbol in message:
    if symbol.upper() in chars_a:
      _lowercase:			int      =     chars_a.find(symbol.upper()				)
      if symbol.isupper():
        translated += chars_b[sym_index].upper()
      else:
        translated += chars_b[sym_index].lower()
    else:
      translated += symbol

  return translated


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     list(UpperCamelCase				)
  random.shuffle(UpperCamelCase				)
  return "".join(UpperCamelCase				)


if __name__ == "__main__":
   main()
