




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()
UpperCAmelCase_									=	logging.get_logger(__name__)


UpperCAmelCase_									=	'https://openaipublic.azureedge.net/jukebox/models/'
UpperCAmelCase_									=	{
    '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 					lowerCamelCase__       (						A__       :					Tuple	):





       '''simple docstring'''



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

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

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

       if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
              __lowerCamelCase   =						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:
              __lowerCamelCase   =						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 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					Dict  ,			A__       :					Optional[Any]  ,			A__       :					List[Any]	):





       '''simple docstring'''



       __lowerCamelCase   =						{}
       import re

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

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

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

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

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

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

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

              elif re_decoder_block_proj_in.fullmatch(A__	):
                     __lowerCamelCase   =						re_decoder_block_proj_in.match(A__	)
                     __lowerCamelCase   =						regex_match.groups()
                     __lowerCamelCase   =						f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
                     __lowerCamelCase   =						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__	):
                     __lowerCamelCase   =						re_prior_cond_conv_out.match(A__	)
                     __lowerCamelCase   =						regex_match.groups()
                     __lowerCamelCase   =						int(groups[1]	) * 2 + int(groups[2]	) - 2
                     __lowerCamelCase   =						f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
                     __lowerCamelCase   =						re_prior_cond_conv_out.sub(A__  ,			A__	)

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

              elif re_prior_cond_proj_in.fullmatch(A__	):
                     __lowerCamelCase   =						re_prior_cond_proj_in.match(A__	)
                     __lowerCamelCase   =						regex_match.groups()
                     __lowerCamelCase   =						f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
                     __lowerCamelCase   =						re_prior_cond_proj_in.sub(A__  ,			A__	)

              # keep original key
              else:
                     __lowerCamelCase   =						original_key

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

              __lowerCamelCase   =						original_key
              __lowerCamelCase   =						value

       return new_dict




@torch.no_grad()
def 					lowerCamelCase__       (						A__       :					str=None  ,			A__       :					List[Any]=None	):





       '''simple docstring'''



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

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

       __lowerCamelCase   =						JukeboxConfig.from_pretrained(A__	)
       __lowerCamelCase   =						JukeboxModel(A__	)

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

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

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

       __lowerCamelCase   =						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__":
       UpperCAmelCase_									=	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.',
       )
       UpperCAmelCase_									=	parser.parse_args()
       convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







from __future__ import annotations

from PIL import Image

# Define glider example
UpperCAmelCase_									=	[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
UpperCAmelCase_									=	[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for i in range(len(A__	)	):
              __lowerCamelCase   =						[]
              for j in range(len(cells[i]	)	):
                     # Get the number of live neighbours
                     __lowerCamelCase   =						0
                     if i > 0 and j > 0:
                            neighbour_count += cells[i - 1][j - 1]
                     if i > 0:
                            neighbour_count += cells[i - 1][j]
                     if i > 0 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i - 1][j + 1]
                     if j > 0:
                            neighbour_count += cells[i][j - 1]
                     if j < len(cells[i]	) - 1:
                            neighbour_count += cells[i][j + 1]
                     if i < len(A__	) - 1 and j > 0:
                            neighbour_count += cells[i + 1][j - 1]
                     if i < len(A__	) - 1:
                            neighbour_count += cells[i + 1][j]
                     if i < len(A__	) - 1 and j < len(cells[i]	) - 1:
                            neighbour_count += cells[i + 1][j + 1]

                     # Rules of the game of life (excerpt from Wikipedia):
                     # 1. Any live cell with two or three live neighbours survives.
                     # 2. Any dead cell with three live neighbours becomes a live cell.
                     # 3. All other live cells die in the next generation.
                     #    Similarly, all other dead cells stay dead.
                     __lowerCamelCase   =						cells[i][j] == 1
                     if (
                         (alive and 2 <= neighbour_count <= 3)
                         or not alive
                         and neighbour_count == 3
                     ):
                            next_generation_row.append(1	)
                     else:
                            next_generation_row.append(0	)

              next_generation.append(A__	)
       return next_generation




def 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



       __lowerCamelCase   =						[]
       for _ in range(A__	):
              # Create output image
              __lowerCamelCase   =						Image.new("""RGB"""  ,			(len(cells[0]	), len(A__	))	)
              __lowerCamelCase   =						img.load()

              # Save cells to image
              for x in range(len(A__	)	):
                     for y in range(len(cells[0]	)	):
                            __lowerCamelCase   =						255 - cells[y][x] * 255
                            __lowerCamelCase   =						(colour, colour, colour)

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						new_generation(A__	)
       return images


if __name__ == "__main__":
       UpperCAmelCase_									=	generate_images(GLIDER, 16)
       images[0].save('out.gif', save_all=True, append_images=images[1:])

