


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_ :str  							=			logging.get_logger(__name__)


a_ :Optional[int]  							=			'https://openaipublic.azureedge.net/jukebox/models/'
a_ :Optional[Any]  							=			{
    '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__							)	-> Optional[Any]:




      '''simple docstring'''

      if key.endswith('''.model.1.bias'''							) and len(key.split('''.'''							)							) > 1_0:
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		key.replace('''.model.1.bias'''      ,    '''.conv1d_1.bias'''							)
      elif key.endswith('''.model.1.weight'''							) and len(key.split('''.'''							)							) > 1_0:
            SCREAMING_SNAKE_CASE__       :  str						     =		key.replace('''.model.1.weight'''      ,    '''.conv1d_1.weight'''							)
      elif key.endswith('''.model.3.bias'''							) and len(key.split('''.'''							)							) > 1_0:
            SCREAMING_SNAKE_CASE__       :  int						     =		key.replace('''.model.3.bias'''      ,    '''.conv1d_2.bias'''							)
      elif key.endswith('''.model.3.weight'''							) and len(key.split('''.'''							)							) > 1_0:
            SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		key.replace('''.model.3.weight'''      ,    '''.conv1d_2.weight'''							)

      if "conditioner_blocks.0." in key:
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		key.replace('''conditioner_blocks.0'''      ,    '''conditioner_blocks'''							)

      if "prime_prior" in key:
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		key.replace('''prime_prior'''      ,    '''encoder'''							)

      if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		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:
            SCREAMING_SNAKE_CASE__       :  List[Any]						     =		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__							)	-> int:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		{}
      import re

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		re.compile(
          r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''							)
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''							)

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		re.compile(
          r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''							)

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

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

            elif re_encoder_block_proj_out.fullmatch(A__							):
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		re_encoder_block_proj_out.match(A__							)
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		regex_match.groups()
                  SCREAMING_SNAKE_CASE__       :  str						     =		f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
                  SCREAMING_SNAKE_CASE__       :  Any						     =		re_encoder_block_proj_out.sub(A__      ,    A__							)

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

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

            elif re_decoder_block_proj_in.fullmatch(A__							):
                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		re_decoder_block_proj_in.match(A__							)
                  SCREAMING_SNAKE_CASE__       :  Dict						     =		regex_match.groups()
                  SCREAMING_SNAKE_CASE__       :  str						     =		f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
                  SCREAMING_SNAKE_CASE__       :  str						     =		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__							):
                  SCREAMING_SNAKE_CASE__       :  int						     =		re_prior_cond_conv_out.match(A__							)
                  SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		regex_match.groups()
                  SCREAMING_SNAKE_CASE__       :  Any						     =		int(groups[1]							) * 2 + int(groups[2]							) - 2
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		re_prior_cond_conv_out.sub(A__      ,    A__							)

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

            elif re_prior_cond_proj_in.fullmatch(A__							):
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		re_prior_cond_proj_in.match(A__							)
                  SCREAMING_SNAKE_CASE__       :  Dict						     =		regex_match.groups()
                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
                  SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		re_prior_cond_proj_in.sub(A__      ,    A__							)

            # keep original key
            else:
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		original_key

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

            SCREAMING_SNAKE_CASE__       :  str						     =		original_key
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		value

      return new_dict




@torch.no_grad()
def    a   (							A__=None      ,    A__=None							)	-> str:




      '''simple docstring'''

      for file in MODEL_MAPPING[model_name]:
            if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'							)[-1]}"""							):
                  SCREAMING_SNAKE_CASE__       :  Tuple						     =		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							)

      SCREAMING_SNAKE_CASE__       :  Tuple						     =		MODEL_MAPPING[model_name.split('''/'''							)[-1]]

      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		JukeboxConfig.from_pretrained(A__							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		JukeboxModel(A__							)

      SCREAMING_SNAKE_CASE__       :  Any						     =		[]
      SCREAMING_SNAKE_CASE__       :  Any						     =		{}
      for i, dict_name in enumerate(A__							):
            SCREAMING_SNAKE_CASE__       :  Dict						     =		torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split('/'							)[-1]}"""							)['''model''']

            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		{}
            for k in old_dic.keys():
                  if k.endswith('''.b'''							):
                        SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		old_dic[k]
                  elif k.endswith('''.w'''							):
                        SCREAMING_SNAKE_CASE__       :  Any						     =		old_dic[k]
                  elif "level_2" not in dict_name and "cond.model." in k:
                        SCREAMING_SNAKE_CASE__       :  Tuple						     =		old_dic[k]
                  else:
                        SCREAMING_SNAKE_CASE__       :  List[Any]						     =		old_dic[k]

            SCREAMING_SNAKE_CASE__       :  Any						     =		'''vqvae''' if i == 0 else f"""priors.{3 - i}"""
            SCREAMING_SNAKE_CASE__       :  str						     =		fix_jukebox_keys(A__      ,    model.state_dict()      ,    A__      ,    A__							)
            weight_dict.append(A__							)

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		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__":
    a_ :Optional[int]  							=			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_ :List[Any]  							=			parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)














"""simple docstring"""
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
			import jax
			import jax.numpy as jnp
			from flax.jax_utils import replicate
			from flax.training.common_utils import shard


@slow
@require_flax
class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		int       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2'
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[str]       		=    FlaxStableDiffusionPipeline.from_pretrained(
										    lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				revision='bf16'   ,				dtype=jnp.bfloataa   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    scheduler_params

										_SCREAMING_SNAKE_CASE   :		int       		=    'A painting of a squirrel eating a burger'
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jax.device_count()
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    num_samples * [prompt]
										_SCREAMING_SNAKE_CASE   :		Any       		=    sd_pipe.prepare_inputs(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    replicate(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    shard(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    jax.random.PRNGKey(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.random.split(lowerCAmelCase_   ,				jax.device_count()							)

										_SCREAMING_SNAKE_CASE   :		int       		=    sd_pipe(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				num_inference_steps=2_5   ,				jit=lowerCAmelCase_							)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										_SCREAMING_SNAKE_CASE   :		str       		=    images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
										_SCREAMING_SNAKE_CASE   :		str       		=    images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.asarray(jax.device_get(image_slice.flatten()							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]							)
										print(F"""output_slice: {output_slice}"""							)
										assert jnp.abs(output_slice - expected_slice							).max() < 1e-2

