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()
SCREAMING_SNAKE_CASE :	Tuple							     =		logging.get_logger(__name__)


SCREAMING_SNAKE_CASE :	List[str]							     =		"""https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE :	Dict							     =		{
    """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   __A		(			_A					):



 """simple docstring"""


 if key.endswith(".model.1.bias"					) and len(key.split("."					)					) > 10:
  __a       =  key.replace(".model.1.bias"							,				".conv1d_1.bias"					)
 elif key.endswith(".model.1.weight"					) and len(key.split("."					)					) > 10:
  __a       =  key.replace(".model.1.weight"							,				".conv1d_1.weight"					)
 elif key.endswith(".model.3.bias"					) and len(key.split("."					)					) > 10:
  __a       =  key.replace(".model.3.bias"							,				".conv1d_2.bias"					)
 elif key.endswith(".model.3.weight"					) and len(key.split("."					)					) > 10:
  __a       =  key.replace(".model.3.weight"							,				".conv1d_2.weight"					)

 if "conditioner_blocks.0." in key:
  __a       =  key.replace("conditioner_blocks.0"							,				"conditioner_blocks"					)

 if "prime_prior" in key:
  __a       =  key.replace("prime_prior"							,				"encoder"					)

 if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
  __a       =  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:
  __a       =  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   __A		(			_A							,				_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  {}
 import re

 __a       =  re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"					)
 __a       =  re.compile(
     r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"					)
 __a       =  re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"					)

 __a       =  re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"					)
 __a       =  re.compile(
     r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"					)
 __a       =  re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"					)

 __a       =  re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"					)
 __a       =  re.compile(
     r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"					)
 __a       =  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(_A					):
   __a       =  re_encoder_block_conv_in.match(_A					)
   __a       =  regex_match.groups()
   __a       =  int(groups[2]					) * 2 + int(groups[3]					)
   __a       =  f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
   __a       =  re_encoder_block_conv_in.sub(_A							,				_A					)

  elif re_encoder_block_resnet.fullmatch(_A					):
   __a       =  re_encoder_block_resnet.match(_A					)
   __a       =  regex_match.groups()
   __a       =  int(groups[2]					) * 2 + int(groups[3]					)
   __a       =  {"1": 1, "3": 2}[groups[-2]]
   __a       =  f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
   __a       =  f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
   __a       =  prefix + resnet_block
   __a       =  re_encoder_block_resnet.sub(_A							,				_A					)

  elif re_encoder_block_proj_out.fullmatch(_A					):
   __a       =  re_encoder_block_proj_out.match(_A					)
   __a       =  regex_match.groups()
   __a       =  f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
   __a       =  re_encoder_block_proj_out.sub(_A							,				_A					)

  # rename vqvae.decoder keys
  elif re_decoder_block_conv_out.fullmatch(_A					):
   __a       =  re_decoder_block_conv_out.match(_A					)
   __a       =  regex_match.groups()
   __a       =  int(groups[2]					) * 2 + int(groups[3]					) - 2
   __a       =  f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
   __a       =  re_decoder_block_conv_out.sub(_A							,				_A					)

  elif re_decoder_block_resnet.fullmatch(_A					):
   __a       =  re_decoder_block_resnet.match(_A					)
   __a       =  regex_match.groups()
   __a       =  int(groups[2]					) * 2 + int(groups[3]					) - 2
   __a       =  {"1": 1, "3": 2}[groups[-2]]
   __a       =  f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
   __a       =  f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
   __a       =  prefix + resnet_block
   __a       =  re_decoder_block_resnet.sub(_A							,				_A					)

  elif re_decoder_block_proj_in.fullmatch(_A					):
   __a       =  re_decoder_block_proj_in.match(_A					)
   __a       =  regex_match.groups()
   __a       =  f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
   __a       =  re_decoder_block_proj_in.sub(_A							,				_A					)

  # rename prior cond.model to upsampler.upsample_block and resnet
  elif re_prior_cond_conv_out.fullmatch(_A					):
   __a       =  re_prior_cond_conv_out.match(_A					)
   __a       =  regex_match.groups()
   __a       =  int(groups[1]					) * 2 + int(groups[2]					) - 2
   __a       =  f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
   __a       =  re_prior_cond_conv_out.sub(_A							,				_A					)

  elif re_prior_cond_resnet.fullmatch(_A					):
   __a       =  re_prior_cond_resnet.match(_A					)
   __a       =  regex_match.groups()
   __a       =  int(groups[1]					) * 2 + int(groups[2]					) - 2
   __a       =  {"1": 1, "3": 2}[groups[-2]]
   __a       =  f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
   __a       =  f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
   __a       =  prefix + resnet_block
   __a       =  re_prior_cond_resnet.sub(_A							,				_A					)

  elif re_prior_cond_proj_in.fullmatch(_A					):
   __a       =  re_prior_cond_proj_in.match(_A					)
   __a       =  regex_match.groups()
   __a       =  f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __a       =  re_prior_cond_proj_in.sub(_A							,				_A					)

  # keep original key
  else:
   __a       =  original_key

  __a       =  replace_key(_A					)

  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:
   __a       =  model_state_dict[f"""{key_prefix}.{key}"""]
   print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""					)
   __a       =  original_key

  __a       =  original_key
  __a       =  value

 return new_dict



@torch.no_grad()
def   __A		(			_A=None							,				_A=None					):



 """simple docstring"""


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

 __a       =  MODEL_MAPPING[model_name.split("/"					)[-1]]

 __a       =  JukeboxConfig.from_pretrained(_A					)
 __a       =  JukeboxModel(_A					)

 __a       =  []
 __a       =  {}
 for i, dict_name in enumerate(_A					):
  __a       =  torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/"					)[-1]}"""					)["model"]

  __a       =  {}
  for k in old_dic.keys():
   if k.endswith(".b"					):
    __a       =  old_dic[k]
   elif k.endswith(".w"					):
    __a       =  old_dic[k]
   elif "level_2" not in dict_name and "cond.model." in k:
    __a       =  old_dic[k]
   else:
    __a       =  old_dic[k]

  __a       =  "vqvae" if i == 0 else f"""priors.{3 - i}"""
  __a       =  fix_jukebox_keys(_A							,				model.state_dict()							,				_A							,				_A					)
  weight_dict.append(_A					)

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

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

 print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""					)
 model.save_pretrained(_A					)

 return weight_dict


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		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.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset



@pytest.mark.parametrize("dataset_size"							,				[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize("input_in_memory_max_size"							,				["default", 0, 100 * 2**20, 900 * 2**20]					)
def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config							,				"IN_MEMORY_MAX_SIZE"							,				_A					)
 __a       =  datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  __a       =  dataset_size < in_memory_max_size
 else:
  __a       =  False
 __a       =  is_small_dataset(_A					)
 assert result == expected


