'''simple docstring'''


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()
__snake_case						  =		logging.get_logger("""transformers.models.encodec""")

__snake_case						  =		{
    """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""",
}
__snake_case						  =		{
    """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""",
}
__snake_case						  =		{
    """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""",
}
__snake_case						  =		{
    """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""",
}
__snake_case						  =		{
    """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""",
}
__snake_case						  =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__snake_case						  =		{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__snake_case						  =		[]
__snake_case						  =		[]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Optional[Any]:
  for attribute in key.split("""."""						):
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  if weight_type is not None:
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						).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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
      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(SCREAMING_SNAKE_CASE_						)[0].split("""."""						)[-2]
          lowercase_     =       mapped_key.replace("""*""" ,	SCREAMING_SNAKE_CASE_						)
        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(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}"""						)

@torch.no_grad()
def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None ,	)			->Dict:
  if config_path is not None:
    lowercase_     =       EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
  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_     =       3_20_00
    lowercase_     =       20_48
    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_     =       4_80_00
    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(SCREAMING_SNAKE_CASE_						)

  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(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       torch.load(SCREAMING_SNAKE_CASE_						)
  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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  model.save_pretrained(SCREAMING_SNAKE_CASE_						)

  if repo_id:
    print("""Pushing to the hub..."""						)
    feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE_						)
    model.push_to_hub(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
       __snake_case						  =		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."""
       )

       __snake_case						  =		parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )


'''simple docstring'''


from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case						  =		10

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  for i in range(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    if array[i] == target:
      return i
  return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  lowercase_     =       0
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  while left <= right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      lowercase_     =       one_third - 1
    elif array[two_third] < target:
      lowercase_     =       two_third + 1

    else:
      lowercase_     =       one_third + 1
      lowercase_     =       two_third - 1
  else:
    return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  if left < right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      return rec_ternary_search(SCREAMING_SNAKE_CASE_ ,	one_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    elif array[two_third] < target:
      return rec_ternary_search(two_third + 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    else:
      return rec_ternary_search(one_third + 1 ,	two_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  else:
    return -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __snake_case						  =		input("""Enter numbers separated by comma:\n""").strip()
       __snake_case						  =		[int(item.strip()) for item in user_input.split(""",""")]
       assert collection == sorted(collection), f"List must be ordered.\n{collection}."
       __snake_case						  =		int(input("""Enter the number to be found in the list:\n""").strip())
       __snake_case						  =		ite_ternary_search(collection, target)
       __snake_case						  =		rec_ternary_search(0, len(collection) - 1, collection, target)
       if resulta != -1:
              print(f'''Iterative search: {target} found at positions: {resulta}''')
              print(f'''Recursive search: {target} found at positions: {resulta}''')
       else:
              print("""Not found""")

