





import math



def 			_a		(       )							->							Tuple:







   """simple docstring"""
   lowerCamelCase__  :		Dict     =				input('''Enter message: '''	)
   lowerCamelCase__  :		List[str]     =				int(input(f"Enter key [2-{len(A__	) - 1}]: "	)	)
   lowerCamelCase__  :		List[Any]     =				input('''Encryption/Decryption [e/d]: '''	)

   if mode.lower().startswith('''e'''	):
      lowerCamelCase__  :		List[Any]     =				encrypt_message(A__     ,    A__	)
   elif mode.lower().startswith('''d'''	):
      lowerCamelCase__  :		Tuple     =				decrypt_message(A__     ,    A__	)

   # Append pipe symbol (vertical bar) to identify spaces at the end.
   print(f"Output:\n{text + '|'}"	)



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							Optional[Any]:







   """simple docstring"""
   lowerCamelCase__  :		int     =				[''''''] * key
   for col in range(A__	):
      lowerCamelCase__  :		Union[str, Any]     =				col
      while pointer < len(A__	):
         cipher_text[col] += message[pointer]
         pointer += key
   return "".join(A__	)



def 			_a		(       UpperCAmelCase     ,    UpperCAmelCase	)							->							Optional[int]:







   """simple docstring"""
   lowerCamelCase__  :		int     =				math.ceil(len(A__	) / key	)
   lowerCamelCase__  :		Optional[int]     =				key
   lowerCamelCase__  :		int     =				(num_cols * num_rows) - len(A__	)
   lowerCamelCase__  :		List[str]     =				[''''''] * num_cols
   lowerCamelCase__  :		Tuple     =				0
   lowerCamelCase__  :		int     =				0

   for symbol in message:
      plain_text[col] += symbol
      col += 1

      if (
          (col == num_cols)
          or (col == num_cols - 1)
          and (row >= num_rows - num_shaded_boxes)
      ):
         lowerCamelCase__  :		Any     =				0
         row += 1

   return "".join(A__	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()








import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
a__   :					List[str] 						=      logging.get_logger("transformers.models.encodec")

a__   :					Optional[Any] 						=      {
    "quantizer.vq.layers.*._codebook.inited": "quantizer.layers.*.codebook.inited",
    "quantizer.vq.layers.*._codebook.cluster_size": "quantizer.layers.*.codebook.cluster_size",
    "quantizer.vq.layers.*._codebook.embed": "quantizer.layers.*.codebook.embed",
    "quantizer.vq.layers.*._codebook.embed_avg": "quantizer.layers.*.codebook.embed_avg",
}
a__   :					int 						=      {
    "encoder.model.0.conv.conv": "encoder.layers.0.conv",
    "encoder.model.1.block.1.conv.conv": "encoder.layers.1.block.1.conv",
    "encoder.model.1.block.3.conv.conv": "encoder.layers.1.block.3.conv",
    "encoder.model.1.shortcut.conv.conv": "encoder.layers.1.shortcut.conv",
    "encoder.model.3.conv.conv": "encoder.layers.3.conv",
    "encoder.model.4.block.1.conv.conv": "encoder.layers.4.block.1.conv",
    "encoder.model.4.block.3.conv.conv": "encoder.layers.4.block.3.conv",
    "encoder.model.4.shortcut.conv.conv": "encoder.layers.4.shortcut.conv",
    "encoder.model.6.conv.conv": "encoder.layers.6.conv",
    "encoder.model.7.block.1.conv.conv": "encoder.layers.7.block.1.conv",
    "encoder.model.7.block.3.conv.conv": "encoder.layers.7.block.3.conv",
    "encoder.model.7.shortcut.conv.conv": "encoder.layers.7.shortcut.conv",
    "encoder.model.9.conv.conv": "encoder.layers.9.conv",
    "encoder.model.10.block.1.conv.conv": "encoder.layers.10.block.1.conv",
    "encoder.model.10.block.3.conv.conv": "encoder.layers.10.block.3.conv",
    "encoder.model.10.shortcut.conv.conv": "encoder.layers.10.shortcut.conv",
    "encoder.model.12.conv.conv": "encoder.layers.12.conv",
    "encoder.model.13.lstm": "encoder.layers.13.lstm",
    "encoder.model.15.conv.conv": "encoder.layers.15.conv",
}
a__   :					int 						=      {
    "encoder.model.0.conv.norm": "encoder.layers.0.norm",
    "encoder.model.1.block.1.conv.norm": "encoder.layers.1.block.1.norm",
    "encoder.model.1.block.3.conv.norm": "encoder.layers.1.block.3.norm",
    "encoder.model.1.shortcut.conv.norm": "encoder.layers.1.shortcut.norm",
    "encoder.model.3.conv.norm": "encoder.layers.3.norm",
    "encoder.model.4.block.1.conv.norm": "encoder.layers.4.block.1.norm",
    "encoder.model.4.block.3.conv.norm": "encoder.layers.4.block.3.norm",
    "encoder.model.4.shortcut.conv.norm": "encoder.layers.4.shortcut.norm",
    "encoder.model.6.conv.norm": "encoder.layers.6.norm",
    "encoder.model.7.block.1.conv.norm": "encoder.layers.7.block.1.norm",
    "encoder.model.7.block.3.conv.norm": "encoder.layers.7.block.3.norm",
    "encoder.model.7.shortcut.conv.norm": "encoder.layers.7.shortcut.norm",
    "encoder.model.9.conv.norm": "encoder.layers.9.norm",
    "encoder.model.10.block.1.conv.norm": "encoder.layers.10.block.1.norm",
    "encoder.model.10.block.3.conv.norm": "encoder.layers.10.block.3.norm",
    "encoder.model.10.shortcut.conv.norm": "encoder.layers.10.shortcut.norm",
    "encoder.model.12.conv.norm": "encoder.layers.12.norm",
    "encoder.model.15.conv.norm": "encoder.layers.15.norm",
}
a__   :					Dict 						=      {
    "decoder.model.0.conv.conv": "decoder.layers.0.conv",
    "decoder.model.1.lstm": "decoder.layers.1.lstm",
    "decoder.model.3.convtr.convtr": "decoder.layers.3.conv",
    "decoder.model.4.block.1.conv.conv": "decoder.layers.4.block.1.conv",
    "decoder.model.4.block.3.conv.conv": "decoder.layers.4.block.3.conv",
    "decoder.model.4.shortcut.conv.conv": "decoder.layers.4.shortcut.conv",
    "decoder.model.6.convtr.convtr": "decoder.layers.6.conv",
    "decoder.model.7.block.1.conv.conv": "decoder.layers.7.block.1.conv",
    "decoder.model.7.block.3.conv.conv": "decoder.layers.7.block.3.conv",
    "decoder.model.7.shortcut.conv.conv": "decoder.layers.7.shortcut.conv",
    "decoder.model.9.convtr.convtr": "decoder.layers.9.conv",
    "decoder.model.10.block.1.conv.conv": "decoder.layers.10.block.1.conv",
    "decoder.model.10.block.3.conv.conv": "decoder.layers.10.block.3.conv",
    "decoder.model.10.shortcut.conv.conv": "decoder.layers.10.shortcut.conv",
    "decoder.model.12.convtr.convtr": "decoder.layers.12.conv",
    "decoder.model.13.block.1.conv.conv": "decoder.layers.13.block.1.conv",
    "decoder.model.13.block.3.conv.conv": "decoder.layers.13.block.3.conv",
    "decoder.model.13.shortcut.conv.conv": "decoder.layers.13.shortcut.conv",
    "decoder.model.15.conv.conv": "decoder.layers.15.conv",
}
a__   :					Any 						=      {
    "decoder.model.0.conv.norm": "decoder.layers.0.norm",
    "decoder.model.3.convtr.norm": "decoder.layers.3.norm",
    "decoder.model.4.block.1.conv.norm": "decoder.layers.4.block.1.norm",
    "decoder.model.4.block.3.conv.norm": "decoder.layers.4.block.3.norm",
    "decoder.model.4.shortcut.conv.norm": "decoder.layers.4.shortcut.norm",
    "decoder.model.6.convtr.norm": "decoder.layers.6.norm",
    "decoder.model.7.block.1.conv.norm": "decoder.layers.7.block.1.norm",
    "decoder.model.7.block.3.conv.norm": "decoder.layers.7.block.3.norm",
    "decoder.model.7.shortcut.conv.norm": "decoder.layers.7.shortcut.norm",
    "decoder.model.9.convtr.norm": "decoder.layers.9.norm",
    "decoder.model.10.block.1.conv.norm": "decoder.layers.10.block.1.norm",
    "decoder.model.10.block.3.conv.norm": "decoder.layers.10.block.3.norm",
    "decoder.model.10.shortcut.conv.norm": "decoder.layers.10.shortcut.norm",
    "decoder.model.12.convtr.norm": "decoder.layers.12.norm",
    "decoder.model.13.block.1.conv.norm": "decoder.layers.13.block.1.norm",
    "decoder.model.13.block.3.conv.norm": "decoder.layers.13.block.3.norm",
    "decoder.model.13.shortcut.conv.norm": "decoder.layers.13.shortcut.norm",
    "decoder.model.15.conv.norm": "decoder.layers.15.norm",
}
a__   :					Tuple 						=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a__   :					List[str] 						=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a__   :					str 						=      []
a__   :					str 						=      []

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__    ,	A__						):
      for attribute in key.split('.'						):
            lowercase__  =	getattr(A__    ,	A__						)

      if weight_type is not None:
            lowercase__  =	getattr(A__    ,	A__						).shape
      else:
            lowercase__  =	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":
            lowercase__  =	value
      elif weight_type == "weight_g":
            lowercase__  =	value
      elif weight_type == "weight_v":
            lowercase__  =	value
      elif weight_type == "bias":
            lowercase__  =	value
      elif weight_type == "running_mean":
            lowercase__  =	value
      elif weight_type == "running_var":
            lowercase__  =	value
      elif weight_type == "num_batches_tracked":
            lowercase__  =	value
      elif weight_type == "weight_ih_l0":
            lowercase__  =	value
      elif weight_type == "weight_hh_l0":
            lowercase__  =	value
      elif weight_type == "bias_ih_l0":
            lowercase__  =	value
      elif weight_type == "bias_hh_l0":
            lowercase__  =	value
      elif weight_type == "weight_ih_l1":
            lowercase__  =	value
      elif weight_type == "weight_hh_l1":
            lowercase__  =	value
      elif weight_type == "bias_ih_l1":
            lowercase__  =	value
      elif weight_type == "bias_hh_l1":
            lowercase__  =	value
      else:
            lowercase__  =	value

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

def 	_lowerCAmelCase (    A__    ,	A__						):
      for key in ignore_keys:
            if key.endswith('.*'						):
                  if name.startswith(key[:-1]						):
                        return True
            elif ".*." in key:
                  lowercase__,						lowercase__  =	key.split('.*.'						)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__						):
      lowercase__  =	[]

      if model_name == "encodec_24khz" or "encodec_32khz":
            lowercase__  =	MAPPING_24K
      elif model_name == "encodec_48khz":
            lowercase__  =	MAPPING_48K
      else:
            raise ValueError(F'''Unsupported model: {model_name}'''						)

      for name, value in orig_dict.items():
            if should_ignore(A__    ,	A__						):
                  logger.info(F'''{name} was ignored'''						)
                  continue

            lowercase__  =	False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        lowercase__,						lowercase__  =	key.split('.*.'						)
                        if prefix in name and suffix in name:
                              lowercase__  =	suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith('embed'						) and name.endswith('embed_avg'						):
                              continue

                        lowercase__  =	True
                        if "*" in mapped_key:
                              lowercase__  =	name.split(A__						)[0].split('.'						)[-2]
                              lowercase__  =	mapped_key.replace('*'    ,	A__						)
                        if "weight_g" in name:
                              lowercase__  =	'weight_g'
                        elif "weight_v" in name:
                              lowercase__  =	'weight_v'
                        elif "weight_ih_l0" in name:
                              lowercase__  =	'weight_ih_l0'
                        elif "weight_hh_l0" in name:
                              lowercase__  =	'weight_hh_l0'
                        elif "bias_ih_l0" in name:
                              lowercase__  =	'bias_ih_l0'
                        elif "bias_hh_l0" in name:
                              lowercase__  =	'bias_hh_l0'
                        elif "weight_ih_l1" in name:
                              lowercase__  =	'weight_ih_l1'
                        elif "weight_hh_l1" in name:
                              lowercase__  =	'weight_hh_l1'
                        elif "bias_ih_l1" in name:
                              lowercase__  =	'bias_ih_l1'
                        elif "bias_hh_l1" in name:
                              lowercase__  =	'bias_hh_l1'
                        elif "bias" in name:
                              lowercase__  =	'bias'
                        elif "weight" in name:
                              lowercase__  =	'weight'
                        elif "running_mean" in name:
                              lowercase__  =	'running_mean'
                        elif "running_var" in name:
                              lowercase__  =	'running_var'
                        elif "num_batches_tracked" in name:
                              lowercase__  =	'num_batches_tracked'
                        else:
                              lowercase__  =	None
                        set_recursively(A__    ,	A__    ,	A__    ,	A__    ,	A__						)
                  continue
            if not is_used:
                  unused_weights.append(A__						)

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

@torch.no_grad()
def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__=None    ,	A__=None    ,	):
      if config_path is not None:
            lowercase__  =	EncodecConfig.from_pretrained(A__						)
      else:
            lowercase__  =	EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            lowercase__  =	[8, 5, 4, 4]
            lowercase__  =	[2.2]
            lowercase__  =	64
            lowercase__  =	32_000
            lowercase__  =	2_048
            lowercase__  =	False
            lowercase__  =	False
            lowercase__  =	False
      elif model_name == "encodec_48khz":
            lowercase__  =	[8, 5, 4, 2]
            lowercase__  =	[3.0, 6.0, 12.0, 24.0]
            lowercase__  =	48_000
            lowercase__  =	2
            lowercase__  =	False
            lowercase__  =	'time_group_norm'
            lowercase__  =	True
            lowercase__  =	1.0
            lowercase__  =	0.01
      else:
            raise ValueError(F'''Unknown model name: {model_name}'''						)

      lowercase__  =	EncodecModel(A__						)

      lowercase__  =	EncodecFeatureExtractor(
          feature_size=config.audio_channels    ,	sampling_rate=config.sampling_rate    ,	chunk_length_s=config.chunk_length_s    ,	overlap=config.overlap    ,	)
      feature_extractor.save_pretrained(A__						)

      lowercase__  =	torch.load(A__						)
      if "best_state" in original_checkpoint:
            # we might have a training state saved, in which case discard the yaml results and just retain the weights
            lowercase__  =	original_checkpoint['best_state']
      recursively_load_weights(A__    ,	A__    ,	A__						)
      model.save_pretrained(A__						)

      if repo_id:
            print('Pushing to the hub...'						)
            feature_extractor.push_to_hub(A__						)
            model.push_to_hub(A__						)


if __name__ == "__main__":
       a__   :					Optional[int] 						=      argparse.ArgumentParser()
       parser.add_argument(
           "--model",
           default="encodec_24khz",
           type=str,
           help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.",
       )
       parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       parser.add_argument(
           "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
       )

       a__   :					Optional[int] 						=      parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )
