

'''simple docstring'''
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict


@pytest.mark.parametrize(
    'files'				,					[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]				,					)
def 			_lowerCAmelCase   (		_UpperCamelCase		:	int				,					_UpperCamelCase		:	int							) ->     int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =tmp_path_factory.mktemp('dset_infos_dir'							)
     if "full:README.md" in files:
          with open(dataset_infos_dir / 'README.md'				,					'w'							) as f:
               f.write('---\ndataset_info:\n  dataset_size: 42\n---'							)
     if "empty:README.md" in files:
          with open(dataset_infos_dir / 'README.md'				,					'w'							) as f:
               f.write(''							)
    # we want to support dataset_infos.json for backward compatibility
     if "dataset_infos.json" in files:
          with open(dataset_infos_dir / 'dataset_infos.json'				,					'w'							) as f:
               f.write('{"default": {"dataset_size": 42}}'							)
     _SCREAMING_SNAKE_CASE				       =DatasetInfosDict.from_directory(lowerCamelCase_							)
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42


@pytest.mark.parametrize(
    'dataset_info'				,					[
        DatasetInfo(),
        DatasetInfo(
            description='foo'				,					features=Features({'a': Value('int32'							)}							)				,					builder_name='builder'				,					config_name='config'				,					version='1.0.0'				,					splits=[{'name': 'train'}]				,					download_size=42				,					),
    ]				,					)
def 			_lowerCAmelCase   (		_UpperCamelCase		:	int				,					_UpperCamelCase		:	DatasetInfo							) ->     Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =str(lowerCamelCase_							)
     dataset_info.write_to_directory(lowerCamelCase_							)
     _SCREAMING_SNAKE_CASE				       =DatasetInfo.from_directory(lowerCamelCase_							)
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(lowerCamelCase_				,					'dataset_info.json'							)							)


def 			_lowerCAmelCase   (		) ->     List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =DatasetInfo(
         description='foo'				,					citation='bar'				,					homepage='https://foo.bar'				,					license='CC0'				,					features=Features({'a': Value('int32'							)}							)				,					post_processed={}				,					supervised_keys=()				,					task_templates=[]				,					builder_name='builder'				,					config_name='config'				,					version='1.0.0'				,					splits=[{'name': 'train', 'num_examples': 42}]				,					download_checksums={}				,					download_size=13_37				,					post_processing_size=4_42				,					dataset_size=12_34				,					size_in_bytes=13_37 + 4_42 + 12_34				,					)
     _SCREAMING_SNAKE_CASE				       =dataset_info._to_yaml_dict()
     assert sorted(lowerCamelCase_							) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML							)
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          assert key in dataset_info_yaml_dict
          assert isinstance(dataset_info_yaml_dict[key]				,					(list, dict, int, str)							)
     _SCREAMING_SNAKE_CASE				       =yaml.safe_dump(lowerCamelCase_							)
     _SCREAMING_SNAKE_CASE				       =yaml.safe_load(lowerCamelCase_							)
     assert dataset_info_yaml_dict == reloaded


def 			_lowerCAmelCase   (		) ->     Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =DatasetInfo()
     _SCREAMING_SNAKE_CASE				       =dataset_info._to_yaml_dict()
     assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    'dataset_infos_dict'				,					[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}							),
        DatasetInfosDict({'my_config_name': DatasetInfo()}							),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'				,					features=Features({'a': Value('int32'							)}							)				,					builder_name='builder'				,					config_name='config'				,					version='1.0.0'				,					splits=[{'name': 'train'}]				,					download_size=42				,					)
            }							),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42							),
                'v2': DatasetInfo(dataset_size=13_37							),
            }							),
    ]				,					)
def 			_lowerCAmelCase   (		_UpperCamelCase		:	Optional[Any]				,					_UpperCamelCase		:	DatasetInfosDict							) ->     Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE				       =str(lowerCamelCase_							)
     dataset_infos_dict.write_to_directory(lowerCamelCase_							)
     _SCREAMING_SNAKE_CASE				       =DatasetInfosDict.from_directory(lowerCamelCase_							)

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          _SCREAMING_SNAKE_CASE				       =config_name
          # the yaml representation doesn't include fields like description or citation
          # so we just test that we can recover what we can from the yaml
          _SCREAMING_SNAKE_CASE				       =DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()							)
     assert dataset_infos_dict == reloaded

     if dataset_infos_dict:
          assert os.path.exists(os.path.join(lowerCamelCase_				,					'README.md'							)							)






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__ :   Tuple						=							logging.get_logger(__name__)


UpperCamelCase__ :   Optional[int]						=							'''https://openaipublic.azureedge.net/jukebox/models/'''
UpperCamelCase__ :   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        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  )       ->  int:







			"""simple docstring"""





			if key.endswith('.model.1.bias'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : List[str]					  =      key.replace('.model.1.bias'  ,							'.conv1d_1.bias'  )
			elif key.endswith('.model.1.weight'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : Any					  =      key.replace('.model.1.weight'  ,							'.conv1d_1.weight'  )
			elif key.endswith('.model.3.bias'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      key.replace('.model.3.bias'  ,							'.conv1d_2.bias'  )
			elif key.endswith('.model.3.weight'  ) and len(key.split('.'  )  ) > 10:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      key.replace('.model.3.weight'  ,							'.conv1d_2.weight'  )

			if "conditioner_blocks.0." in key:
						SCREAMING_SNAKE_CASE_   : List[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_   : Union[str, Any]					  =      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_   : Union[str, 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        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     List[Any]  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     List[Any]  )       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      {}
			import re

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      re.compile(
			    R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : str					  =      re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

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

			SCREAMING_SNAKE_CASE_   : Dict					  =      re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      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(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : List[str]					  =      re_encoder_block_conv_in.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : int					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : List[str]					  =      int(groups[2]  ) * 2 + int(groups[3]  )
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Optional[int]					  =      re_encoder_block_conv_in.sub(lowerCamelCase_  ,							lowerCamelCase_  )

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

						elif re_encoder_block_proj_out.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Tuple					  =      re_encoder_block_proj_out.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : int					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : int					  =      F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Tuple					  =      re_encoder_block_proj_out.sub(lowerCamelCase_  ,							lowerCamelCase_  )

						# rename vqvae.decoder keys
						elif re_decoder_block_conv_out.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Any					  =      re_decoder_block_conv_out.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : Any					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      int(groups[2]  ) * 2 + int(groups[3]  ) - 2
									SCREAMING_SNAKE_CASE_   : Optional[int]					  =      F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Optional[int]					  =      re_decoder_block_conv_out.sub(lowerCamelCase_  ,							lowerCamelCase_  )

						elif re_decoder_block_resnet.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Dict					  =      re_decoder_block_resnet.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : str					  =      int(groups[2]  ) * 2 + int(groups[3]  ) - 2
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {'1': 1, '3': 2}[groups[-2]]
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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_   : Union[str, Any]					  =      prefix + resnet_block
									SCREAMING_SNAKE_CASE_   : Dict					  =      re_decoder_block_resnet.sub(lowerCamelCase_  ,							lowerCamelCase_  )

						elif re_decoder_block_proj_in.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      re_decoder_block_proj_in.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : int					  =      F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      re_decoder_block_proj_in.sub(lowerCamelCase_  ,							lowerCamelCase_  )

						# rename prior cond.model to upsampler.upsample_block and resnet
						elif re_prior_cond_conv_out.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : List[str]					  =      re_prior_cond_conv_out.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : str					  =      int(groups[1]  ) * 2 + int(groups[2]  ) - 2
									SCREAMING_SNAKE_CASE_   : Any					  =      F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      re_prior_cond_conv_out.sub(lowerCamelCase_  ,							lowerCamelCase_  )

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

						elif re_prior_cond_proj_in.fullmatch(lowerCamelCase_  ):
									SCREAMING_SNAKE_CASE_   : Any					  =      re_prior_cond_proj_in.match(lowerCamelCase_  )
									SCREAMING_SNAKE_CASE_   : List[str]					  =      regex_match.groups()
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      re_prior_cond_proj_in.sub(lowerCamelCase_  ,							lowerCamelCase_  )

						# keep original key
						else:
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      original_key

						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      replace_key(lowerCamelCase_  )

						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_   : 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_   : Dict					  =      original_key

						SCREAMING_SNAKE_CASE_   : int					  =      original_key
						SCREAMING_SNAKE_CASE_   : int					  =      value

			return new_dict





@torch.no_grad()
def        __UpperCAmelCase						(							lowerCamelCase_ :     int=None  ,							lowerCamelCase_ :     int=None  )       ->  Dict:







			"""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_   : int					  =      requests.get(F'{PREFIX}{file}'  ,							allow_redirects=lowerCamelCase_  )
									os.makedirs(F'{pytorch_dump_folder_path}/'  ,							exist_ok=lowerCamelCase_  )
									open(F'{pytorch_dump_folder_path}/{file.split("/"  )[-1]}'  ,							'wb'  ).write(r.content  )

			SCREAMING_SNAKE_CASE_   : List[str]					  =      MODEL_MAPPING[model_name.split('/'  )[-1]]

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      JukeboxConfig.from_pretrained(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : str					  =      JukeboxModel(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      []
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      {}
			for i, dict_name in enumerate(lowerCamelCase_  ):
						SCREAMING_SNAKE_CASE_   : str					  =      torch.load(F'{pytorch_dump_folder_path}/{dict_name.split("/"  )[-1]}'  )['model']

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

						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'vqvae' if i == 0 else F'priors.{3 - i}'
						SCREAMING_SNAKE_CASE_   : Any					  =      fix_jukebox_keys(lowerCamelCase_  ,							model.state_dict()  ,							lowerCamelCase_  ,							lowerCamelCase_  )
						weight_dict.append(lowerCamelCase_  )

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

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

			print(F'Saving model {model_name} to {pytorch_dump_folder_path}'  )
			model.save_pretrained(lowerCamelCase_  )

			return weight_dict


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



