




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_snake_case            = {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_snake_case            = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_,snake_case_  ):



      for attribute in key.split("""."""  ):
            _A							:  Any    =		getattr(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE  )

      if weight_type is not None:
            _A							:  str    =		getattr(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE  ).shape
      else:
            _A							:  Any    =		hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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":
            _A							:  Tuple    =		value
      elif weight_type == "weight_g":
            _A							:  Any    =		value
      elif weight_type == "weight_v":
            _A							:  Any    =		value
      elif weight_type == "bias":
            _A							:  List[Any]    =		value
      elif weight_type == "running_mean":
            _A							:  Any    =		value
      elif weight_type == "running_var":
            _A							:  str    =		value
      elif weight_type == "num_batches_tracked":
            _A							:  List[str]    =		value
      elif weight_type == "inv_freq":
            _A							:  Any    =		value
      else:
            _A							:  List[str]    =		value

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







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  List[Any]    =		[]
      _A							:  Dict    =		fairseq_model.state_dict()

      _A							:  Dict    =		hf_model.wavaveca_conformer.feature_extractor

      for name, value in fairseq_dict.items():
            _A							:  List[Any]    =		False
            if "conv_layers" in name:
                  load_conv_layer(
                      _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,hf_model.config.feat_extract_norm == """group""",)
                  _A							:  Tuple    =		True
            else:
                  for key, mapped_key in MAPPING.items():
                        _A							:  Dict    =		"""wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
                              _A							:  List[str]    =		True
                              if "*" in mapped_key:
                                    _A							:  Dict    =		name.split(_SCREAMING_SNAKE_CASE  )[0].split("""."""  )[-2]
                                    _A							:  Optional[int]    =		mapped_key.replace("""*""",_SCREAMING_SNAKE_CASE  )
                              if "pos_bias_u" in name:
                                    _A							:  Tuple    =		None
                              elif "pos_bias_v" in name:
                                    _A							:  List[Any]    =		None
                              elif "weight_g" in name:
                                    _A							:  Union[str, Any]    =		"""weight_g"""
                              elif "weight_v" in name:
                                    _A							:  str    =		"""weight_v"""
                              elif "bias" in name:
                                    _A							:  Any    =		"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    _A							:  Union[str, Any]    =		"""weight"""
                              elif "running_mean" in name:
                                    _A							:  List[str]    =		"""running_mean"""
                              elif "inv_freq" in name:
                                    _A							:  List[Any]    =		"""inv_freq"""
                              elif "running_var" in name:
                                    _A							:  Dict    =		"""running_var"""
                              elif "num_batches_tracked" in name:
                                    _A							:  Any    =		"""num_batches_tracked"""
                              else:
                                    _A							:  List[Any]    =		None
                              set_recursively(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE  )
                        continue
            if not is_used:
                  unused_weights.append(_SCREAMING_SNAKE_CASE  )

      logger.warning(f'''Unused weights: {unused_weights}'''  )







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		full_name.split("""conv_layers."""  )[-1]
      _A							:  Tuple    =		name.split("""."""  )
      _A							:  Dict    =		int(items[0]  )
      _A							:  List[str]    =		int(items[1]  )

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''  )
                  _A							:  Optional[Any]    =		value
                  logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''  )
                  _A							:  List[str]    =		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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''  )
                  _A							:  str    =		value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            f'''{full_name} has size {value.shape}, but'''
                            f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''  )
                  _A							:  List[Any]    =		value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
      else:
            unused_weights.append(_SCREAMING_SNAKE_CASE  )







@torch.no_grad()
def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_=None,snake_case_=None,snake_case_=True  ):



      if config_path is not None:
            _A							:  Optional[int]    =		WavaVecaConformerConfig.from_pretrained(_SCREAMING_SNAKE_CASE,hidden_act="""swish"""  )
      else:
            _A							:  List[str]    =		WavaVecaConformerConfig()

      if "rope" in checkpoint_path:
            _A							:  List[Any]    =		"""rotary"""

      if is_finetuned:
            if dict_path:
                  _A							:  Tuple    =		Dictionary.load(_SCREAMING_SNAKE_CASE  )

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  _A							:  Optional[int]    =		target_dict.pad_index
                  _A							:  Any    =		target_dict.bos_index
                  _A							:  List[str]    =		target_dict.eos_index
                  _A							:  Tuple    =		len(target_dict.symbols  )
                  _A							:  Any    =		os.path.join(_SCREAMING_SNAKE_CASE,"""vocab.json"""  )
                  if not os.path.isdir(_SCREAMING_SNAKE_CASE  ):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE  )  )
                        return
                  os.makedirs(_SCREAMING_SNAKE_CASE,exist_ok=_SCREAMING_SNAKE_CASE  )
                  _A							:  Tuple    =		target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  _A							:  str    =		0
                  _A							:  List[str]    =		1
                  with open(_SCREAMING_SNAKE_CASE,"""w""",encoding="""utf-8"""  ) as vocab_handle:
                        json.dump(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE  )
                  _A							:  str    =		WavaVecaCTCTokenizer(
                      _SCREAMING_SNAKE_CASE,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=_SCREAMING_SNAKE_CASE,)
                  _A							:  List[str]    =		True if config.feat_extract_norm == """layer""" else False
                  _A							:  List[str]    =		WavaVecaFeatureExtractor(
                      feature_size=1,sampling_rate=16000,padding_value=0,do_normalize=_SCREAMING_SNAKE_CASE,return_attention_mask=_SCREAMING_SNAKE_CASE,)
                  _A							:  Union[str, Any]    =		WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE,tokenizer=_SCREAMING_SNAKE_CASE  )
                  processor.save_pretrained(_SCREAMING_SNAKE_CASE  )

            _A							:  int    =		WavaVecaConformerForCTC(_SCREAMING_SNAKE_CASE  )
      else:
            _A							:  Tuple    =		WavaVecaConformerForPreTraining(_SCREAMING_SNAKE_CASE  )

      if is_finetuned:
            _A    ,     _A    ,     _A							:  str    =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path],arg_overrides={"""data""": """/""".join(dict_path.split("""/"""  )[:-1]  )}  )
      else:
            _A							:  List[Any]    =		argparse.Namespace(task="""audio_pretraining"""  )
            _A							:  Optional[Any]    =		fairseq.tasks.setup_task(_SCREAMING_SNAKE_CASE  )

            _A    ,     _A    ,     _A							:  str    =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],task=_SCREAMING_SNAKE_CASE  )

      _A							:  Optional[int]    =		model[0].eval()

      recursively_load_weights(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,not is_finetuned  )

      hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
   _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("--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"
   )
   _snake_case            = parser.parse_args()
   convert_wavaveca_conformer_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Union[str, Any]    =		""""""
      for i in table:
            res += inp[i - 1]
      return res







def        lowerCAmelCase_			(  snake_case_  ):
      return data[1:] + data[0]







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Dict    =		""""""
      for i in range(len(snake_case_  )  ):
            if a[i] == b[i]:
                  res += "0"
            else:
                  res += "1"
      return res







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  int    =		int("""0b""" + data[0] + data[-1],2  )
      _A							:  Any    =		int("""0b""" + data[1:3],2  )
      return bin(s[row][col]  )[2:]







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_,snake_case_,snake_case_  ):
      _A							:  List[str]    =		message[:4]
      _A							:  List[Any]    =		message[4:]
      _A							:  Union[str, Any]    =		apply_table(snake_case_,snake_case_  )
      _A							:  List[Any]    =		xor(snake_case_,snake_case_  )
      _A							:  Optional[Any]    =		apply_sbox(snake_case_,temp[:4]  )  # noqa: E741
      _A							:  List[Any]    =		apply_sbox(snake_case_,temp[4:]  )
      _A							:  int    =		"""0""" * (2 - len(snake_case_  )) + l  # noqa: E741
      _A							:  Union[str, Any]    =		"""0""" * (2 - len(snake_case_  )) + r
      _A							:  List[Any]    =		apply_table(l + r,snake_case_  )
      _A							:  Any    =		xor(snake_case_,snake_case_  )
      return temp + right


if __name__ == "__main__":
  _snake_case            = input("Enter 10 bit key: ")
  _snake_case            = input("Enter 8 bit message: ")

  _snake_case            = [6, 3, 7, 4, 8, 5, 10, 9]
  _snake_case            = [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
  _snake_case            = [2, 4, 3, 1]
  _snake_case            = [2, 6, 3, 1, 4, 8, 5, 7]
  _snake_case            = [4, 1, 3, 5, 7, 2, 8, 6]
  _snake_case            = [4, 1, 2, 3, 2, 3, 4, 1]
  _snake_case            = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
  _snake_case            = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

  # key generation
  _snake_case            = apply_table(key, paa_table)
  _snake_case            = temp[:5]
  _snake_case            = temp[5:]
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = apply_table(left + right, pa_table)
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = left_shift(left)
  _snake_case            = left_shift(right)
  _snake_case            = apply_table(left + right, pa_table)

  # encryption
  _snake_case            = apply_table(message, IP)
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = temp[4:] + temp[:4]
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = apply_table(temp, IP_inv)
  print("Cipher text is:", CT)

  # decryption
  _snake_case            = apply_table(CT, IP)
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = temp[4:] + temp[:4]
  _snake_case            = function(expansion, sa, sa, keya, temp)
  _snake_case            = apply_table(temp, IP_inv)
  print("Plain text after decypting is:", PT)

