
"""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()
_snake_case          =						logging.get_logger(__name__)


_snake_case          =						'''https://openaipublic.azureedge.net/jukebox/models/'''
_snake_case          =						{
    '''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 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""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 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[Any]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""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(SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			re_encoder_block_conv_in.match(SCREAMING_SNAKE_CASE						)
																					_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(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

														elif re_encoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			re_encoder_block_resnet.match(SCREAMING_SNAKE_CASE						)
																					_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(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

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

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			re_decoder_block_conv_out.match(SCREAMING_SNAKE_CASE						)
																					_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(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

														elif re_decoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			re_decoder_block_resnet.match(SCREAMING_SNAKE_CASE						)
																					_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(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

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

														# rename prior cond.model to upsampler.upsample_block and resnet
														elif re_prior_cond_conv_out.fullmatch(SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			re_prior_cond_conv_out.match(SCREAMING_SNAKE_CASE						)
																					_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(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

														elif re_prior_cond_resnet.fullmatch(SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			re_prior_cond_resnet.match(SCREAMING_SNAKE_CASE						)
																					_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(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

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

														# keep original key
														else:
																					_lowerCAmelCase  	=			original_key

														_lowerCAmelCase  	=			replace_key(SCREAMING_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:
																					_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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any=None     ,  SCREAMING_SNAKE_CASE:					Optional[int]=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=SCREAMING_SNAKE_CASE						)
																					os.makedirs(f"""{pytorch_dump_folder_path}/"""     ,  exist_ok=SCREAMING_SNAKE_CASE						)
																					open(f"""{pytorch_dump_folder_path}/{file.split('/'						)[-1]}"""     ,  'wb'						).write(r.content						)

							_lowerCAmelCase  	=			MODEL_MAPPING[model_name.split('/'						)[-1]]

							_lowerCAmelCase  	=			JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			JukeboxModel(SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			[]
							_lowerCAmelCase  	=			{}
							for i, dict_name in enumerate(SCREAMING_SNAKE_CASE						):
														_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(SCREAMING_SNAKE_CASE     ,  model.state_dict()     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														weight_dict.append(SCREAMING_SNAKE_CASE						)

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

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

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

							return weight_dict


if __name__ == "__main__":
	_snake_case          =						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.''',
	)
	_snake_case          =						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 .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			Any  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			10


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			[1, 2, 3, 4]
											_lowerCAmelCase  	=			[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
											self.assertEqual(truncate_or_pad(UpperCAmelCase_				,     self.block_size				,     0  )				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(UpperCAmelCase_				,     self.block_size				,     0  )				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(UpperCAmelCase_				,     self.block_size				,     0  )				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			str  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
											_lowerCAmelCase	,		_lowerCAmelCase  	=			process_story(UpperCAmelCase_  )
											self.assertEqual(UpperCAmelCase_				,     []  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			''
											_lowerCAmelCase	,		_lowerCAmelCase  	=			process_story(UpperCAmelCase_  )
											self.assertEqual(UpperCAmelCase_				,     []  )
											self.assertEqual(UpperCAmelCase_				,     []  )


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			(
											    'It was the year of Our Lord one thousand seven hundred and '
											    'seventy-five\n\nSpiritual revelations were conceded to England '
											    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
											)
											_lowerCAmelCase	,		_lowerCAmelCase  	=			process_story(UpperCAmelCase_  )

											_lowerCAmelCase  	=			[
											    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
											    'Spiritual revelations were conceded to England at that favoured period, as at this.',
											]
											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			['It was the best of times.']
											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.tensor([1, 2, 3, 4]  )
											_lowerCAmelCase  	=			torch.tensor([1, 1, 1, 1]  )
											np.testing.assert_array_equal(build_mask(UpperCAmelCase_				,     0  ).numpy()				,     expected.numpy()  )


				def 				__lowerCamelCase	(					self	:			int  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.tensor([1, 2, 3, 4, 23, 23, 23]  )
											_lowerCAmelCase  	=			torch.tensor([1, 1, 1, 1, 0, 0, 0]  )
											np.testing.assert_array_equal(build_mask(UpperCAmelCase_				,     23  ).numpy()				,     expected.numpy()  )


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







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.tensor([8, 2, 3, 4, 1, 1, 1]  )
											_lowerCAmelCase  	=			torch.tensor([1, 1, 1, 1, 0, 0, 0]  )
											np.testing.assert_array_equal(build_mask(UpperCAmelCase_				,     1  ).numpy()				,     expected.numpy()  )







				def 				__lowerCamelCase	(					self	:			str  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			101
											_lowerCAmelCase  	=			torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]  )
											_lowerCAmelCase  	=			torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]  )

											_lowerCAmelCase  	=			compute_token_type_ids(UpperCAmelCase_				,     UpperCAmelCase_  )
											np.testing.assert_array_equal(UpperCAmelCase_				,     UpperCAmelCase_  )






