


"""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()
UpperCamelCase__				:Optional[int]			   =	logging.get_logger(__name__)


UpperCamelCase__				:Union[str, Any]			   =	"""https://openaipublic.azureedge.net/jukebox/models/"""
UpperCamelCase__				: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_       ( snake_case__		)					->  Optional[int]:
			if key.endswith('''.model.1.bias'''		) and len(key.split('''.'''		)		) > 10:
						_UpperCamelCase					:Dict					     =	key.replace('''.model.1.bias'''       ,					'''.conv1d_1.bias'''		)
			elif key.endswith('''.model.1.weight'''		) and len(key.split('''.'''		)		) > 10:
						_UpperCamelCase					:Tuple					     =	key.replace('''.model.1.weight'''       ,					'''.conv1d_1.weight'''		)
			elif key.endswith('''.model.3.bias'''		) and len(key.split('''.'''		)		) > 10:
						_UpperCamelCase					:List[Any]					     =	key.replace('''.model.3.bias'''       ,					'''.conv1d_2.bias'''		)
			elif key.endswith('''.model.3.weight'''		) and len(key.split('''.'''		)		) > 10:
						_UpperCamelCase					:List[Any]					     =	key.replace('''.model.3.weight'''       ,					'''.conv1d_2.weight'''		)

			if "conditioner_blocks.0." in key:
						_UpperCamelCase					:Any					     =	key.replace('''conditioner_blocks.0'''       ,					'''conditioner_blocks'''		)

			if "prime_prior" in key:
						_UpperCamelCase					: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:
						_UpperCamelCase					:List[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:
						_UpperCamelCase					:Dict					     =	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_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  Tuple:
			_UpperCamelCase					:Optional[int]					     =	{}
			import re

			_UpperCamelCase					:Tuple					     =	re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''		)
			_UpperCamelCase					:List[Any]					     =	re.compile(
			    R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''		)
			_UpperCamelCase					:int					     =	re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''		)

			_UpperCamelCase					:Union[str, Any]					     =	re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''		)
			_UpperCamelCase					:Dict					     =	re.compile(
			    R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''		)
			_UpperCamelCase					:Dict					     =	re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''		)

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

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

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

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

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

						elif re_decoder_block_proj_in.fullmatch(snake_case__		):
									_UpperCamelCase					:str					     =	re_decoder_block_proj_in.match(snake_case__		)
									_UpperCamelCase					:Optional[Any]					     =	regex_match.groups()
									_UpperCamelCase					:Optional[Any]					     =	f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
									_UpperCamelCase					:Tuple					     =	re_decoder_block_proj_in.sub(snake_case__       ,					snake_case__		)

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

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

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

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

						_UpperCamelCase					:Dict					     =	replace_key(snake_case__		)

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

						_UpperCamelCase					:Optional[Any]					     =	original_key
						_UpperCamelCase					:Any					     =	value

			return new_dict





@torch.no_grad()
def 				A_       ( snake_case__=None       ,					snake_case__=None		)					->  int:
			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/'		)[-1]}"		):
									_UpperCamelCase					:int					     =	requests.get(f"{PREFIX}{file}"       ,					allow_redirects=snake_case__		)
									os.makedirs(f"{pytorch_dump_folder_path}/"       ,					exist_ok=snake_case__		)
									open(f"{pytorch_dump_folder_path}/{file.split('/'		)[-1]}"       ,					'''wb'''		).write(r.content		)

			_UpperCamelCase					:int					     =	MODEL_MAPPING[model_name.split('''/'''		)[-1]]

			_UpperCamelCase					:int					     =	JukeboxConfig.from_pretrained(snake_case__		)
			_UpperCamelCase					:List[Any]					     =	JukeboxModel(snake_case__		)

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

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

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

			_UpperCamelCase					:Optional[int]					     =	weight_dict.pop(0		)
			model.vqvae.load_state_dict(snake_case__		)
			for i in range(len(snake_case__		)		):
						model.priors[i].load_state_dict(weight_dict[2 - i]		)

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

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

			return weight_dict


if __name__ == "__main__":
					UpperCamelCase__				: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.""",
					)
					UpperCamelCase__				:List[str]			   =	parser.parse_args()
					convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      A(  unittest.TestCase		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	UNetaDModel(
					    block_out_channels=(32, 64)					,				layers_per_block=2					,				sample_size=32					,				in_channels=3					,				out_channels=3					,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')					,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')					,				)
					return model





		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[Any]					     =	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2






@slow
@require_torch
class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2

