
"""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()
_a      :			Dict					=      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      :			Tuple					=      {
    """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      :			Dict					=      {
    """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      :			Tuple					=      {
    """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      :			List[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      :			List[Any]					=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_a      :			Dict					=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_a      :			Any					=      []
_a      :			Dict					=      []
def 				a__   (   a   :				Union[str, Any]       ,     a   :				Dict       ,     a   :				Tuple       ,     a   :				List[Any]       ,     a   :				Optional[int]		):

  """simple docstring"""







  for attribute in key.split("."		):
    _snake_case     :						int						=   getattr(a       ,     a		)

  if weight_type is not None:
    _snake_case     :						Any						=   getattr(a       ,     a		).shape
  else:
    _snake_case     :						List[str]						=   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":
    _snake_case     :						Any						=   value
  elif weight_type == "weight_g":
    _snake_case     :						str						=   value
  elif weight_type == "weight_v":
    _snake_case     :						List[Any]						=   value
  elif weight_type == "bias":
    _snake_case     :						int						=   value
  elif weight_type == "running_mean":
    _snake_case     :						Any						=   value
  elif weight_type == "running_var":
    _snake_case     :						str						=   value
  elif weight_type == "num_batches_tracked":
    _snake_case     :						int						=   value
  elif weight_type == "weight_ih_l0":
    _snake_case     :						int						=   value
  elif weight_type == "weight_hh_l0":
    _snake_case     :						Optional[Any]						=   value
  elif weight_type == "bias_ih_l0":
    _snake_case     :						Optional[Any]						=   value
  elif weight_type == "bias_hh_l0":
    _snake_case     :						List[str]						=   value
  elif weight_type == "weight_ih_l1":
    _snake_case     :						Tuple						=   value
  elif weight_type == "weight_hh_l1":
    _snake_case     :						Tuple						=   value
  elif weight_type == "bias_ih_l1":
    _snake_case     :						Optional[Any]						=   value
  elif weight_type == "bias_hh_l1":
    _snake_case     :						Dict						=   value
  else:
    _snake_case     :						str						=   value

  logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'		)
def 				a__   (   a   :				int       ,     a   :				str		):

  """simple docstring"""







  for key in ignore_keys:
    if key.endswith(".*"		):
      if name.startswith(key[:-1]		):
        return True
    elif ".*." in key:
      _snake_case     :						List[str]						=   key.split(".*."		)
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False
def 				a__   (   a   :				str       ,     a   :				Union[str, Any]       ,     a   :				Any		):

  """simple docstring"""







  _snake_case     :						int						=   []

  if model_name == "encodec_24khz" or "encodec_32khz":
    _snake_case     :						Optional[Any]						=   MAPPING_24K
  elif model_name == "encodec_48khz":
    _snake_case     :						str						=   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

    _snake_case     :						Tuple						=   False
    for key, mapped_key in MAPPING.items():
      if "*" in key:
        _snake_case     :						List[str]						=   key.split(".*."		)
        if prefix in name and suffix in name:
          _snake_case     :						str						=   suffix

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

        _snake_case     :						Optional[int]						=   True
        if "*" in mapped_key:
          _snake_case     :						int						=   name.split(a		)[0].split("."		)[-2]
          _snake_case     :						Optional[int]						=   mapped_key.replace("*"       ,     a		)
        if "weight_g" in name:
          _snake_case     :						Optional[int]						=   "weight_g"
        elif "weight_v" in name:
          _snake_case     :						Any						=   "weight_v"
        elif "weight_ih_l0" in name:
          _snake_case     :						Dict						=   "weight_ih_l0"
        elif "weight_hh_l0" in name:
          _snake_case     :						Dict						=   "weight_hh_l0"
        elif "bias_ih_l0" in name:
          _snake_case     :						Any						=   "bias_ih_l0"
        elif "bias_hh_l0" in name:
          _snake_case     :						Union[str, Any]						=   "bias_hh_l0"
        elif "weight_ih_l1" in name:
          _snake_case     :						Optional[Any]						=   "weight_ih_l1"
        elif "weight_hh_l1" in name:
          _snake_case     :						str						=   "weight_hh_l1"
        elif "bias_ih_l1" in name:
          _snake_case     :						Optional[Any]						=   "bias_ih_l1"
        elif "bias_hh_l1" in name:
          _snake_case     :						int						=   "bias_hh_l1"
        elif "bias" in name:
          _snake_case     :						Union[str, Any]						=   "bias"
        elif "weight" in name:
          _snake_case     :						Optional[int]						=   "weight"
        elif "running_mean" in name:
          _snake_case     :						List[str]						=   "running_mean"
        elif "running_var" in name:
          _snake_case     :						int						=   "running_var"
        elif "num_batches_tracked" in name:
          _snake_case     :						Union[str, Any]						=   "num_batches_tracked"
        else:
          _snake_case     :						Union[str, Any]						=   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 				a__   (   a   :				Optional[Any]       ,     a   :				Tuple       ,     a   :				Union[str, Any]       ,     a   :				List[Any]=None       ,     a   :				Optional[int]=None       ,     ):

  """simple docstring"""







  if config_path is not None:
    _snake_case     :						int						=   EncodecConfig.from_pretrained(a		)
  else:
    _snake_case     :						Optional[Any]						=   EncodecConfig()

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

  _snake_case     :						Optional[int]						=   EncodecModel(a		)

  _snake_case     :						Union[str, Any]						=   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		)

  _snake_case     :						Union[str, Any]						=   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
    _snake_case     :						int						=   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      :			List[Any]					=      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      :			Tuple					=      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






class      _UpperCAmelCase				:
    def __init__(    self						,    snake_case_						,    snake_case_     ):
      _snake_case			,					_snake_case     :						Dict						=   text, pattern
      _snake_case			,					_snake_case     :						int						=   len(snake_case_     ), len(snake_case_     )
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if char == self.pattern[i]:
          return i
      return -1
    def 					lowerCamelCase__	(    self						,    snake_case_     ):

      for i in range(self.patLen - 1						,    -1						,    -1     ):
        if self.pattern[i] != self.text[current_pos + i]:
          return current_pos + i
      return -1


    def 					lowerCamelCase__	(    self     ):
      # searches pattern in text and returns index positions
      _snake_case     :						List[str]						=   []
      for i in range(self.textLen - self.patLen + 1     ):
        _snake_case     :						Union[str, Any]						=   self.mismatch_in_text(snake_case_     )
        if mismatch_index == -1:
          positions.append(snake_case_     )
        else:
          _snake_case     :						Tuple						=   self.match_in_pattern(self.text[mismatch_index]     )
          _snake_case     :						Tuple						=   (
              mismatch_index - match_index
          )  # shifting index lgtm [py/multiple-definition]
      return positions


_a      :			List[Any]					=							"""ABAABA"""
_a      :			str					=							"""AB"""
_a      :			List[Any]					=							BoyerMooreSearch(text, pattern)
_a      :			Any					=							bms.bad_character_heuristic()

if len(positions) == 0:
    print("""No match found""")
else:
    print("""Pattern found in following positions: """)
    print(positions)

