





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()
__lowerCamelCase :  Union[str, Any]										=  logging.get_logger("""transformers.models.encodec""")

__lowerCamelCase :  str										=  {
    """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""",
}
__lowerCamelCase :  Dict										=  {
    """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""",
}
__lowerCamelCase :  Union[str, Any]										=  {
    """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""",
}
__lowerCamelCase :  Optional[Any]										=  {
    """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""",
}
__lowerCamelCase :  Union[str, 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""",
}
__lowerCamelCase :  Union[str, Any]										=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowerCamelCase :  int										=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowerCamelCase :  List[Any]										=  []
__lowerCamelCase :  Any										=  []



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				List[Any]:
 for attribute in key.split("."  ):
  UpperCamelCase			:      int 			=     getattr(_lowerCAmelCase							,     _lowerCAmelCase  )

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

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



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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     []

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

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

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

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

    UpperCamelCase			:      Dict 			=     True
    if "*" in mapped_key:
     UpperCamelCase			:      str 			=     name.split(_lowerCAmelCase  )[0].split("."  )[-2]
     UpperCamelCase			:      Optional[int] 			=     mapped_key.replace("*"							,     _lowerCAmelCase  )
    if "weight_g" in name:
     UpperCamelCase			:      Dict 			=     "weight_g"
    elif "weight_v" in name:
     UpperCamelCase			:      List[Any] 			=     "weight_v"
    elif "weight_ih_l0" in name:
     UpperCamelCase			:      str 			=     "weight_ih_l0"
    elif "weight_hh_l0" in name:
     UpperCamelCase			:      Optional[Any] 			=     "weight_hh_l0"
    elif "bias_ih_l0" in name:
     UpperCamelCase			:      Optional[int] 			=     "bias_ih_l0"
    elif "bias_hh_l0" in name:
     UpperCamelCase			:      Union[str, Any] 			=     "bias_hh_l0"
    elif "weight_ih_l1" in name:
     UpperCamelCase			:      List[Any] 			=     "weight_ih_l1"
    elif "weight_hh_l1" in name:
     UpperCamelCase			:      Optional[Any] 			=     "weight_hh_l1"
    elif "bias_ih_l1" in name:
     UpperCamelCase			:      int 			=     "bias_ih_l1"
    elif "bias_hh_l1" in name:
     UpperCamelCase			:      Any 			=     "bias_hh_l1"
    elif "bias" in name:
     UpperCamelCase			:      int 			=     "bias"
    elif "weight" in name:
     UpperCamelCase			:      List[Any] 			=     "weight"
    elif "running_mean" in name:
     UpperCamelCase			:      int 			=     "running_mean"
    elif "running_var" in name:
     UpperCamelCase			:      Union[str, Any] 			=     "running_var"
    elif "num_batches_tracked" in name:
     UpperCamelCase			:      Tuple 			=     "num_batches_tracked"
    else:
     UpperCamelCase			:      int 			=     None
    set_recursively(_lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )
   continue
  if not is_used:
   unused_weights.append(_lowerCAmelCase  )

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



@torch.no_grad()
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase=None							,     _lowerCAmelCase=None							,     )		->				Dict:
 if config_path is not None:
  UpperCamelCase			:      Optional[Any] 			=     EncodecConfig.from_pretrained(_lowerCAmelCase  )
 else:
  UpperCamelCase			:      List[Any] 			=     EncodecConfig()

 if model_name == "encodec_24khz":
  pass  # config is already correct
 elif model_name == "encodec_32khz":
  UpperCamelCase			:      Dict 			=     [8, 5, 4, 4]
  UpperCamelCase			:      Dict 			=     [2.2]
  UpperCamelCase			:      Tuple 			=     64
  UpperCamelCase			:      Dict 			=     3_2000
  UpperCamelCase			:      str 			=     2048
  UpperCamelCase			:      Union[str, Any] 			=     False
  UpperCamelCase			:      Tuple 			=     False
  UpperCamelCase			:      Optional[Any] 			=     False
 elif model_name == "encodec_48khz":
  UpperCamelCase			:      Optional[int] 			=     [8, 5, 4, 2]
  UpperCamelCase			:      str 			=     [3.0, 6.0, 12.0, 24.0]
  UpperCamelCase			:      int 			=     4_8000
  UpperCamelCase			:      List[str] 			=     2
  UpperCamelCase			:      Union[str, Any] 			=     False
  UpperCamelCase			:      Optional[int] 			=     "time_group_norm"
  UpperCamelCase			:      str 			=     True
  UpperCamelCase			:      str 			=     1.0
  UpperCamelCase			:      List[str] 			=     0.01
 else:
  raise ValueError(F"""Unknown model name: {model_name}"""  )

 UpperCamelCase			:      Tuple 			=     EncodecModel(_lowerCAmelCase  )

 UpperCamelCase			:      Optional[int] 			=     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(_lowerCAmelCase  )

 UpperCamelCase			:      List[Any] 			=     torch.load(_lowerCAmelCase  )
 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
  UpperCamelCase			:      str 			=     original_checkpoint["best_state"]
 recursively_load_weights(_lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )
 model.save_pretrained(_lowerCAmelCase  )

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


if __name__ == "__main__":
    __lowerCamelCase :  List[str]										=  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."""
    )

    __lowerCamelCase :  List[str]										=  parser.parse_args()
    convert_checkpoint(
        args.model,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )







from __future__ import annotations

from random import random
from typing import Generic, TypeVar

__lowerCamelCase :  Dict										=  TypeVar("""KT""")
__lowerCamelCase :  Dict										=  TypeVar("""VT""")



class       A__     ( Generic[KT, VT]    ):





      def __init__(				self ,							A_ = "root" ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     key
       UpperCamelCase			:      List[Any] 			=     value
       UpperCamelCase			:      list[Node[KT, VT]] 			=     []





      def __repr__(				self     ):
       '''simple docstring'''




       return F"""Node({self.key}: {self.value})"""





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return len(self.forward     )



class       A__     ( Generic[KT, VT]    ):





      def __init__(				self ,							A_ = 0.5 ,							A_ = 16     ):
       '''simple docstring'''




       UpperCamelCase			:      Node[KT, VT] 			=     Node[KT, VT]()
       UpperCamelCase			:      List[Any] 			=     0
       UpperCamelCase			:      Union[str, Any] 			=     p
       UpperCamelCase			:      List[str] 			=     max_level





      def __str__(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     list(self     )

       if len(A_     ) == 0:
        return F"""SkipList(level={self.level})"""

       UpperCamelCase			:      str 			=     max((len(str(A_     )     ) for item in items) ,							default=4     )
       UpperCamelCase			:      Dict 			=     max(A_ ,							4     ) + 4

       UpperCamelCase			:      str 			=     self.head
       UpperCamelCase			:      List[Any] 			=     []

       UpperCamelCase			:      int 			=     node.forward.copy()
       lines.append(F"""[{node.key}]""".ljust(A_ ,							"-"     ) + "* " * len(A_     )     )
       lines.append(" " * label_size + "| " * len(A_     )     )

       while len(node.forward     ) != 0:
        UpperCamelCase			:      Union[str, Any] 			=     node.forward[0]

        lines.append(
            F"""[{node.key}]""".ljust(A_ ,							"-"     )
            + " ".join(str(n.key     ) if n.key == node.key else "|" for n in forwards     )     )
        lines.append(" " * label_size + "| " * len(A_     )     )
        UpperCamelCase			:      Tuple 			=     node.forward

       lines.append("None".ljust(A_     ) + "* " * len(A_     )     )
       return F"""SkipList(level={self.level})\n""" + "\n".join(A_     )





      def __iter__(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     self.head

       while len(node.forward     ) != 0:
        yield node.forward[0].key
        UpperCamelCase			:      Union[str, Any] 			=     node.forward[0]





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     1
       while random() < self.p and level < self.max_level:
        level += 1

       return level





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     []

       UpperCamelCase			:      List[Any] 			=     self.head

       for i in reversed(range(self.level     )     ):
        # i < node.level - When node level is lesser than `i` decrement `i`.
        # node.forward[i].key < key - Jumping to node with key value higher
        #                             or equal to searched key would result
        #                             in skipping searched key.
        while i < node.level and node.forward[i].key < key:
         UpperCamelCase			:      str 			=     node.forward[i]
        # Each leftmost node (relative to searched node) will potentially have to
        # be updated.
        update_vector.append(A_     )

       update_vector.reverse()  # Note that we were inserting values in reverse order.

       # len(node.forward) != 0 - If current node doesn't contain any further
       #                          references then searched key is not present.
       # node.forward[0].key == key - Next node key should be equal to search key
       #                              if key is present.
       if len(node.forward     ) != 0 and node.forward[0].key == key:
        return node.forward[0], update_vector
       else:
        return None, update_vector





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      str 			=     self._locate_node(A_     )

       if node is not None:
        for i, update_node in enumerate(A_     ):
         # Remove or replace all references to removed node.
         if update_node.level > i and update_node.forward[i].key == key:
          if node.level > i:
           UpperCamelCase			:      Tuple 			=     node.forward[i]
          else:
           UpperCamelCase			:      List[Any] 			=     update_node.forward[:i]





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      Optional[int] 			=     self._locate_node(A_     )
       if node is not None:
        UpperCamelCase			:      Union[str, Any] 			=     value
       else:
        UpperCamelCase			:      Dict 			=     self.random_level()

        if level > self.level:
         # After level increase we have to add additional nodes to head.
         for _ in range(self.level - 1 ,							A_     ):
          update_vector.append(self.head     )
         UpperCamelCase			:      Optional[int] 			=     level

        UpperCamelCase			:      Dict 			=     Node(A_ ,							A_     )

        for i, update_node in enumerate(update_vector[:level]     ):
         # Change references to pass through new node.
         if update_node.level > i:
          new_node.forward.append(update_node.forward[i]     )

         if update_node.level < i + 1:
          update_node.forward.append(A_     )
         else:
          UpperCamelCase			:      List[Any] 			=     new_node





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			,					UpperCamelCase			:      Union[str, Any] 			=     self._locate_node(A_     )

       if node is not None:
        return node.value

       return None



def 	A_       (  )		->				List[Any]:
 UpperCamelCase			:      int 			=     SkipList()
 skip_list.insert("Key1"							,     3  )
 skip_list.insert("Key2"							,     12  )
 skip_list.insert("Key3"							,     41  )
 skip_list.insert("Key4"							,     -19  )

 UpperCamelCase			:      Optional[int] 			=     skip_list.head
 UpperCamelCase			:      List[str] 			=     {}
 while node.level != 0:
  UpperCamelCase			:      str 			=     node.forward[0]
  UpperCamelCase			:      Optional[int] 			=     node.value

 assert len(_lowerCAmelCase  ) == 4
 assert all_values["Key1"] == 3
 assert all_values["Key2"] == 12
 assert all_values["Key3"] == 41
 assert all_values["Key4"] == -19



def 	A_       (  )		->				List[Any]:
 UpperCamelCase			:      Optional[int] 			=     SkipList()
 skip_list.insert("Key1"							,     10  )
 skip_list.insert("Key1"							,     12  )

 skip_list.insert("Key5"							,     7  )
 skip_list.insert("Key7"							,     10  )
 skip_list.insert("Key10"							,     5  )

 skip_list.insert("Key7"							,     7  )
 skip_list.insert("Key5"							,     5  )
 skip_list.insert("Key10"							,     10  )

 UpperCamelCase			:      Dict 			=     skip_list.head
 UpperCamelCase			:      Tuple 			=     {}
 while node.level != 0:
  UpperCamelCase			:      List[str] 			=     node.forward[0]
  UpperCamelCase			:      Dict 			=     node.value

 if len(_lowerCAmelCase  ) != 4:
  print()
 assert len(_lowerCAmelCase  ) == 4
 assert all_values["Key1"] == 12
 assert all_values["Key7"] == 7
 assert all_values["Key5"] == 5
 assert all_values["Key10"] == 10



def 	A_       (  )		->				List[Any]:
 UpperCamelCase			:      List[Any] 			=     SkipList()
 assert skip_list.find("Some key"  ) is None



def 	A_       (  )		->				Tuple:
 UpperCamelCase			:      Optional[int] 			=     SkipList()

 skip_list.insert("Key2"							,     20  )
 assert skip_list.find("Key2"  ) == 20

 skip_list.insert("Some Key"							,     10  )
 skip_list.insert("Key2"							,     8  )
 skip_list.insert("V"							,     13  )

 assert skip_list.find("Y"  ) is None
 assert skip_list.find("Key2"  ) == 8
 assert skip_list.find("Some Key"  ) == 10
 assert skip_list.find("V"  ) == 13



def 	A_       (  )		->				Dict:
 UpperCamelCase			:      Optional[int] 			=     SkipList()
 skip_list.delete("Some key"  )

 assert len(skip_list.head.forward  ) == 0



def 	A_       (  )		->				Dict:
 UpperCamelCase			:      List[Any] 			=     SkipList()

 skip_list.insert("Key1"							,     12  )
 skip_list.insert("V"							,     13  )
 skip_list.insert("X"							,     14  )
 skip_list.insert("Key2"							,     15  )

 skip_list.delete("V"  )
 skip_list.delete("Key2"  )

 assert skip_list.find("V"  ) is None
 assert skip_list.find("Key2"  ) is None



def 	A_       (  )		->				List[str]:
 UpperCamelCase			:      int 			=     SkipList()

 skip_list.insert("Key1"							,     12  )
 skip_list.insert("V"							,     13  )
 skip_list.insert("X"							,     14  )
 skip_list.insert("Key2"							,     15  )

 skip_list.delete("V"  )
 assert skip_list.find("V"  ) is None
 assert skip_list.find("X"  ) == 14
 assert skip_list.find("Key1"  ) == 12
 assert skip_list.find("Key2"  ) == 15

 skip_list.delete("X"  )
 assert skip_list.find("V"  ) is None
 assert skip_list.find("X"  ) is None
 assert skip_list.find("Key1"  ) == 12
 assert skip_list.find("Key2"  ) == 15

 skip_list.delete("Key1"  )
 assert skip_list.find("V"  ) is None
 assert skip_list.find("X"  ) is None
 assert skip_list.find("Key1"  ) is None
 assert skip_list.find("Key2"  ) == 15

 skip_list.delete("Key2"  )
 assert skip_list.find("V"  ) is None
 assert skip_list.find("X"  ) is None
 assert skip_list.find("Key1"  ) is None
 assert skip_list.find("Key2"  ) is None



def 	A_       (  )		->				List[Any]:
 UpperCamelCase			:      List[Any] 			=     SkipList()

 skip_list.insert("Key1"							,     12  )
 skip_list.insert("V"							,     13  )
 skip_list.insert("X"							,     142  )
 skip_list.insert("Key2"							,     15  )

 skip_list.delete("X"  )

 def traverse_keys(_lowerCAmelCase  ):
  yield node.key
  for forward_node in node.forward:
   yield from traverse_keys(_lowerCAmelCase  )

 assert len(set(traverse_keys(skip_list.head  )  )  ) == 4



def 	A_       (  )		->				Union[str, Any]:
 def is_sorted(_lowerCAmelCase  ):
  return all(next_item >= item for item, next_item in zip(_lowerCAmelCase							,     lst[1:]  )  )

 UpperCamelCase			:      int 			=     SkipList()
 for i in range(10  ):
  skip_list.insert(_lowerCAmelCase							,     _lowerCAmelCase  )
 assert is_sorted(list(_lowerCAmelCase  )  )
 skip_list.delete(5  )
 skip_list.delete(8  )
 skip_list.delete(2  )
 assert is_sorted(list(_lowerCAmelCase  )  )
 skip_list.insert(-12							,     -12  )
 skip_list.insert(77							,     77  )
 assert is_sorted(list(_lowerCAmelCase  )  )



def 	A_       (  )		->				Tuple:
 for _ in range(100  ):
  # Repeat test 100 times due to the probabilistic nature of skip list
  # random values == random bugs
  test_insert()
  test_insert_overrides_existing_value()

  test_searching_empty_list_returns_none()
  test_search()

  test_deleting_item_from_empty_list_do_nothing()
  test_deleted_items_are_not_founded_by_find_method()
  test_delete_removes_only_given_key()
  test_delete_doesnt_leave_dead_nodes()

  test_iter_always_yields_sorted_values()



def 	A_       (  )		->				List[str]:
 UpperCamelCase			:      Optional[int] 			=     SkipList()
 skip_list.insert(2							,     "2"  )
 skip_list.insert(4							,     "4"  )
 skip_list.insert(6							,     "4"  )
 skip_list.insert(4							,     "5"  )
 skip_list.insert(8							,     "4"  )
 skip_list.insert(9							,     "4"  )

 skip_list.delete(4  )

 print(_lowerCAmelCase  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
