
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


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


a_											=						"https://openaipublic.azureedge.net/jukebox/models/"
a_											=						{
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if key.endswith(".model.1.bias"       ) and len(key.split("."       )       ) > 1_0:
        snake_case_ :    int							=						key.replace(".model.1.bias",		".conv1d_1.bias"       )
    elif key.endswith(".model.1.weight"       ) and len(key.split("."       )       ) > 1_0:
        snake_case_ :    int							=						key.replace(".model.1.weight",		".conv1d_1.weight"       )
    elif key.endswith(".model.3.bias"       ) and len(key.split("."       )       ) > 1_0:
        snake_case_ :    Dict							=						key.replace(".model.3.bias",		".conv1d_2.bias"       )
    elif key.endswith(".model.3.weight"       ) and len(key.split("."       )       ) > 1_0:
        snake_case_ :    List[str]							=						key.replace(".model.3.weight",		".conv1d_2.weight"       )

    if "conditioner_blocks.0." in key:
        snake_case_ :    Optional[Any]							=						key.replace("conditioner_blocks.0",		"conditioner_blocks"       )

    if "prime_prior" in key:
        snake_case_ :    Optional[int]							=						key.replace("prime_prior",		"encoder"       )

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        snake_case_ :    str							=						key.replace(".emb.",		"."       )

    if key.endswith("k"       ):  # replace vqvae.X.k with vqvae.X.codebook
        return key.replace(".k",		".codebook"       )
    if "y_emb." in key:
        return key.replace("y_emb.",		"metadata_embedding."       )

    if "x_emb.emb." in key:
        snake_case_ :    Tuple							=						key.replace("0.x_emb.emb",		"embed_tokens"       )

    if "prime_state_ln" in key:
        return key.replace("prime_state_ln",		"encoder.final_layer_norm"       )
    if ".ln" in key:
        return key.replace(".ln",		".layer_norm"       )
    if "_ln" in key:
        return key.replace("_ln",		"_layer_norm"       )

    if "prime_state_proj" in key:
        return key.replace("prime_state_proj",		"encoder.proj_in"       )
    if "prime_x_out" in key:
        return key.replace("prime_x_out",		"encoder.lm_head"       )
    if "prior.x_out" in key:
        return key.replace("x_out",		"fc_proj_out"       )
    if "x_emb" in key:
        return key.replace("x_emb",		"embed_tokens"       )

    return key

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[Any]							=						{}
    import re

    snake_case_ :    Dict							=						re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"       )
    snake_case_ :    Optional[int]							=						re.compile(
        r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
    snake_case_ :    str							=						re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"       )

    snake_case_ :    Optional[int]							=						re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"       )
    snake_case_ :    List[str]							=						re.compile(
        r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
    snake_case_ :    str							=						re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"       )

    snake_case_ :    Union[str, Any]							=						re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"       )
    snake_case_ :    Optional[Any]							=						re.compile(
        r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"       )
    snake_case_ :    Any							=						re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"       )

    for original_key, value in state_dict.items():
        # rename vqvae.encoder keys
        if re_encoder_block_conv_in.fullmatch(__lowercase       ):
            snake_case_ :    Optional[int]							=						re_encoder_block_conv_in.match(__lowercase       )
            snake_case_ :    Dict							=						regex_match.groups()
            snake_case_ :    List[Any]							=						int(groups[2]       ) * 2 + int(groups[3]       )
            snake_case_ :    Optional[int]							=						f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
            snake_case_ :    Optional[Any]							=						re_encoder_block_conv_in.sub(__lowercase,		__lowercase       )

        elif re_encoder_block_resnet.fullmatch(__lowercase       ):
            snake_case_ :    Any							=						re_encoder_block_resnet.match(__lowercase       )
            snake_case_ :    Dict							=						regex_match.groups()
            snake_case_ :    Optional[int]							=						int(groups[2]       ) * 2 + int(groups[3]       )
            snake_case_ :    Any							=						{'1': 1, '3': 2}[groups[-2]]
            snake_case_ :    Tuple							=						f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
            snake_case_ :    Tuple							=						f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            snake_case_ :    str							=						prefix + resnet_block
            snake_case_ :    Union[str, Any]							=						re_encoder_block_resnet.sub(__lowercase,		__lowercase       )

        elif re_encoder_block_proj_out.fullmatch(__lowercase       ):
            snake_case_ :    Union[str, Any]							=						re_encoder_block_proj_out.match(__lowercase       )
            snake_case_ :    Any							=						regex_match.groups()
            snake_case_ :    Optional[Any]							=						f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
            snake_case_ :    Dict							=						re_encoder_block_proj_out.sub(__lowercase,		__lowercase       )

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(__lowercase       ):
            snake_case_ :    Union[str, Any]							=						re_decoder_block_conv_out.match(__lowercase       )
            snake_case_ :    Dict							=						regex_match.groups()
            snake_case_ :    List[str]							=						int(groups[2]       ) * 2 + int(groups[3]       ) - 2
            snake_case_ :    str							=						f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
            snake_case_ :    List[str]							=						re_decoder_block_conv_out.sub(__lowercase,		__lowercase       )

        elif re_decoder_block_resnet.fullmatch(__lowercase       ):
            snake_case_ :    Tuple							=						re_decoder_block_resnet.match(__lowercase       )
            snake_case_ :    int							=						regex_match.groups()
            snake_case_ :    Tuple							=						int(groups[2]       ) * 2 + int(groups[3]       ) - 2
            snake_case_ :    List[str]							=						{'1': 1, '3': 2}[groups[-2]]
            snake_case_ :    List[Any]							=						f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
            snake_case_ :    Optional[int]							=						f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            snake_case_ :    List[str]							=						prefix + resnet_block
            snake_case_ :    List[str]							=						re_decoder_block_resnet.sub(__lowercase,		__lowercase       )

        elif re_decoder_block_proj_in.fullmatch(__lowercase       ):
            snake_case_ :    str							=						re_decoder_block_proj_in.match(__lowercase       )
            snake_case_ :    Any							=						regex_match.groups()
            snake_case_ :    List[Any]							=						f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
            snake_case_ :    str							=						re_decoder_block_proj_in.sub(__lowercase,		__lowercase       )

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(__lowercase       ):
            snake_case_ :    Tuple							=						re_prior_cond_conv_out.match(__lowercase       )
            snake_case_ :    List[Any]							=						regex_match.groups()
            snake_case_ :    Dict							=						int(groups[1]       ) * 2 + int(groups[2]       ) - 2
            snake_case_ :    Union[str, Any]							=						f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
            snake_case_ :    Optional[Any]							=						re_prior_cond_conv_out.sub(__lowercase,		__lowercase       )

        elif re_prior_cond_resnet.fullmatch(__lowercase       ):
            snake_case_ :    Optional[Any]							=						re_prior_cond_resnet.match(__lowercase       )
            snake_case_ :    Optional[int]							=						regex_match.groups()
            snake_case_ :    Any							=						int(groups[1]       ) * 2 + int(groups[2]       ) - 2
            snake_case_ :    int							=						{'1': 1, '3': 2}[groups[-2]]
            snake_case_ :    Optional[int]							=						f'conditioner_blocks.upsampler.upsample_block.{block_index}.'
            snake_case_ :    Optional[int]							=						f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            snake_case_ :    List[str]							=						prefix + resnet_block
            snake_case_ :    str							=						re_prior_cond_resnet.sub(__lowercase,		__lowercase       )

        elif re_prior_cond_proj_in.fullmatch(__lowercase       ):
            snake_case_ :    Any							=						re_prior_cond_proj_in.match(__lowercase       )
            snake_case_ :    int							=						regex_match.groups()
            snake_case_ :    Optional[int]							=						f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
            snake_case_ :    List[Any]							=						re_prior_cond_proj_in.sub(__lowercase,		__lowercase       )

        # keep original key
        else:
            snake_case_ :    Optional[int]							=						original_key

        snake_case_ :    List[Any]							=						replace_key(__lowercase       )

        if f'{key_prefix}.{key}' not in model_state_dict or key is None:
            print(f'failed converting {original_key} to {key}, does not match'       )

        # handle missmatched shape
        elif value.shape != model_state_dict[f'{key_prefix}.{key}'].shape:
            snake_case_ :    Optional[int]							=						model_state_dict[f'{key_prefix}.{key}']
            print(f'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'       )
            snake_case_ :    Dict							=						original_key

        snake_case_ :    str							=						original_key
        snake_case_ :    int							=						value

    return new_dict

@torch.no_grad()
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE=None,		__SCREAMING_SNAKE_CASE=None       ):
    """simple docstring"""






    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/"       )[-1]}'       ):
            snake_case_ :    Union[str, Any]							=						requests.get(f'{PREFIX}{file}',		allow_redirects=__lowercase       )
            os.makedirs(f'{pytorch_dump_folder_path}/',		exist_ok=__lowercase       )
            open(f'{pytorch_dump_folder_path}/{file.split("/"       )[-1]}',		"wb"       ).write(r.content       )

    snake_case_ :    List[str]							=						MODEL_MAPPING[model_name.split("/"       )[-1]]

    snake_case_ :    Any							=						JukeboxConfig.from_pretrained(__lowercase       )
    snake_case_ :    str							=						JukeboxModel(__lowercase       )

    snake_case_ :    Optional[Any]							=						[]
    snake_case_ :    Union[str, Any]							=						{}
    for i, dict_name in enumerate(__lowercase       ):
        snake_case_ :    Tuple							=						torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"       )[-1]}'       )['model']

        snake_case_ :    Union[str, Any]							=						{}
        for k in old_dic.keys():
            if k.endswith(".b"       ):
                snake_case_ :    Union[str, Any]							=						old_dic[k]
            elif k.endswith(".w"       ):
                snake_case_ :    Dict							=						old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                snake_case_ :    Optional[Any]							=						old_dic[k]
            else:
                snake_case_ :    Optional[int]							=						old_dic[k]

        snake_case_ :    Tuple							=						'vqvae' if i == 0 else f'priors.{3 - i}'
        snake_case_ :    Union[str, Any]							=						fix_jukebox_keys(__lowercase,		model.state_dict(),		__lowercase,		__lowercase       )
        weight_dict.append(__lowercase       )

    snake_case_ :    Any							=						weight_dict.pop(0       )
    model.vqvae.load_state_dict(__lowercase       )
    for i in range(len(__lowercase       )       ):
        model.priors[i].load_state_dict(weight_dict[2 - i]       )

    Path(__lowercase       ).mkdir(exist_ok=__lowercase       )
    with open(f'{pytorch_dump_folder_path}/mapping.json',		"w"       ) as txtfile:
        json.dump(__lowercase,		__lowercase       )

    print(f'Saving model {model_name} to {pytorch_dump_folder_path}'       )
    model.save_pretrained(__lowercase       )

    return weight_dict


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="jukebox-5b-lyrics",
      type=str,
      help="Name of the model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="jukebox-5b-lyrics-converted",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  a_											=						parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

a_											=						TypeVar("T")
a_											=						TypeVar("U")






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Any     			=						key
     snake_case_ :    List[Any]     			=						val
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None





 def __repr__(    self):
     return (
         F'Node: key: {self.key}, val: {self.val}, '
         F'has next: {bool(self.next)}, has prev: {bool(self.prev)}'
     )






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self):
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_			, snake_case_ :    Union[str, Any]     			=						self.rear, self.head





 def __repr__(    self):
     snake_case_ :    Dict     			=						["DoubleLinkedList"]
     snake_case_ :    Dict     			=						self.head
     while node.next is not None:
         rep.append(str(lowercase_))
         snake_case_ :    List[str]     			=						node.next
     rep.append(str(self.rear))
     return ",\n    ".join(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[str]     			=						self.rear.prev

     # All nodes other than self.head are guaranteed to have non-None previous
     assert previous is not None

     snake_case_ :    Tuple     			=						node
     snake_case_ :    str     			=						previous
     snake_case_ :    Optional[Any]     			=						node
     snake_case_ :    Any     			=						self.rear





 def        snake_case__		(    self ,       lowercase_):

     if node.prev is None or node.next is None:
         return None

     snake_case_ :    Union[str, Any]     			=						node.next
     snake_case_ :    Optional[int]     			=						node.prev
     snake_case_ :    str     			=						None
     snake_case_ :    int     			=						None
     return node






class    UpperCAmelCase_				(	Generic[T, U]					):
 UpperCAmelCase_		    =						{}





 def __init__(    self ,       lowercase_):
     snake_case_ :    DoubleLinkedList[T, U]     			=						DoubleLinkedList()
     snake_case_ :    List[str]     			=						capacity
     snake_case_ :    Any     			=						0
     snake_case_ :    Dict     			=						0
     snake_case_ :    Union[str, Any]     			=						0
     snake_case_ :    dict[T, DoubleLinkedListNode[T, U]]     			=						{}





 def __repr__(    self):
     return (
         F'CacheInfo(hits={self.hits}, misses={self.miss}, '
         F'capacity={self.capacity}, current size={self.num_keys})'
     )





 def __contains__(    self ,       lowercase_):
     return key in self.cache





 def        snake_case__		(    self ,       lowercase_):
     # Note: pythonic interface would throw KeyError rather than return None

     if key in self.cache:
         self.hits += 1
         snake_case_ :    DoubleLinkedListNode[T, U]     			=						self.cache[key]
         snake_case_ :    Optional[Any]     			=						self.list.remove(self.cache[key])
         assert node == value_node

         # node is guaranteed not None because it is in self.cache
         assert node is not None
         self.list.add(lowercase_)
         return node.val
     self.miss += 1
     return None





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):

     if key not in self.cache:
         if self.num_keys >= self.capacity:
             # delete first node (oldest) when over capacity
             snake_case_ :    Optional[Any]     			=						self.list.head.next

             # guaranteed to have a non-None first node when num_keys > 0
             # explain to type checker via assertions
             assert first_node is not None
             assert first_node.key is not None
             assert (
                 self.list.remove(lowercase_) is not None
             )  # node guaranteed to be in list assert node.key is not None

             del self.cache[first_node.key]
             self.num_keys -= 1
         snake_case_ :    Dict     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
         self.list.add(self.cache[key])
         self.num_keys += 1

     else:
         # bump node to the end of the list, update value
         snake_case_ :    List[Any]     			=						self.list.remove(self.cache[key])
         assert node is not None  # node guaranteed to be in list
         snake_case_ :    Optional[Any]     			=						value
         self.list.add(lowercase_)





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ = 1_28):

     def cache_decorator_inner(lowercase_) -> Callable[..., U]:
         def cache_decorator_wrapper(*lowercase_) -> U:
             if func not in cls.decorator_function_to_instance_map:
                 snake_case_ :    List[str]     			=						LRUCache(lowercase_)

             snake_case_ :    Optional[Any]     			=						cls.decorator_function_to_instance_map[func].get(args[0])
             if result is None:
                 snake_case_ :    Any     			=						func(*lowercase_)
                 cls.decorator_function_to_instance_map[func].put(args[0] ,       lowercase_)
             return result

         def cache_info() -> LRUCache[T, U]:
             return cls.decorator_function_to_instance_map[func]

         setattr(lowercase_ ,       "cache_info" ,       lowercase_)  # noqa: B010

         return cache_decorator_wrapper

     return cache_decorator_inner


if __name__ == "__main__":
  import doctest

  doctest.testmod()




