





'''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()
lowercase__    :      Dict						=    logging.get_logger(__name__)


lowercase__    :      Any						=    '''https://openaipublic.azureedge.net/jukebox/models/'''
lowercase__    :      List[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        _lowerCAmelCase			(  __snake_case  : List[Any]			)     ->     Any:
     if key.endswith('.model.1.bias'			) and len(key.split('.'			)			) > 10:
          __A						: List[str]      =       key.replace('.model.1.bias' ,							'.conv1d_1.bias'			)
     elif key.endswith('.model.1.weight'			) and len(key.split('.'			)			) > 10:
          __A						: List[str]      =       key.replace('.model.1.weight' ,							'.conv1d_1.weight'			)
     elif key.endswith('.model.3.bias'			) and len(key.split('.'			)			) > 10:
          __A						: Optional[Any]      =       key.replace('.model.3.bias' ,							'.conv1d_2.bias'			)
     elif key.endswith('.model.3.weight'			) and len(key.split('.'			)			) > 10:
          __A						: int      =       key.replace('.model.3.weight' ,							'.conv1d_2.weight'			)

     if "conditioner_blocks.0." in key:
          __A						: Optional[Any]      =       key.replace('conditioner_blocks.0' ,							'conditioner_blocks'			)

     if "prime_prior" in key:
          __A						: 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:
          __A						: List[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:
          __A						: 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        _lowerCAmelCase			(  __snake_case  : Dict ,							__snake_case  : Tuple ,							__snake_case  : List[Any] ,							__snake_case  : Any			)     ->     List[Any]:
     __A						: Dict      =       {}
     import re

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

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

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

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

          elif re_encoder_block_proj_out.fullmatch(__snake_case			):
               __A						: Union[str, Any]      =       re_encoder_block_proj_out.match(__snake_case			)
               __A						: Any      =       regex_match.groups()
               __A						: Optional[Any]      =       f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
               __A						: List[str]      =       re_encoder_block_proj_out.sub(__snake_case ,							__snake_case			)

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

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

          elif re_decoder_block_proj_in.fullmatch(__snake_case			):
               __A						: Optional[Any]      =       re_decoder_block_proj_in.match(__snake_case			)
               __A						: Any      =       regex_match.groups()
               __A						: Any      =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
               __A						: List[Any]      =       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			):
               __A						: Tuple      =       re_prior_cond_conv_out.match(__snake_case			)
               __A						: Any      =       regex_match.groups()
               __A						: Optional[Any]      =       int(groups[1]			) * 2 + int(groups[2]			) - 2
               __A						: List[Any]      =       f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
               __A						: Optional[Any]      =       re_prior_cond_conv_out.sub(__snake_case ,							__snake_case			)

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

          elif re_prior_cond_proj_in.fullmatch(__snake_case			):
               __A						: int      =       re_prior_cond_proj_in.match(__snake_case			)
               __A						: List[Any]      =       regex_match.groups()
               __A						: Dict      =       f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
               __A						: List[Any]      =       re_prior_cond_proj_in.sub(__snake_case ,							__snake_case			)

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

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

          __A						: List[str]      =       original_key
          __A						: str      =       value

     return new_dict






@torch.no_grad()
def        _lowerCAmelCase			(  __snake_case  : List[str]=None ,							__snake_case  : List[Any]=None			)     ->     List[str]:
     for file in MODEL_MAPPING[model_name]:
          if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/"			)[-1]}'			):
               __A						: Dict      =       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			)

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

     __A						: Any      =       JukeboxConfig.from_pretrained(__snake_case			)
     __A						: str      =       JukeboxModel(__snake_case			)

     __A						: Optional[int]      =       []
     __A						: Optional[Any]      =       {}
     for i, dict_name in enumerate(__snake_case			):
          __A						: Optional[Any]      =       torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"			)[-1]}'			)['model']

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

          __A						: Dict      =       'vqvae' if i == 0 else f'priors.{3 - i}'
          __A						: int      =       fix_jukebox_keys(__snake_case ,							model.state_dict() ,							__snake_case ,							__snake_case			)
          weight_dict.append(__snake_case			)

     __A						: Tuple      =       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__":
  lowercase__    :      Dict						=    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.''',
  )
  lowercase__    :      List[Any]						=    parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






'''simple docstring'''




import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer


class  SCREAMING_SNAKE_CASE    (a__							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       tempfile.mkdtemp()
          __A						: str      =       5

          # Realm tok
          __A						: Union[str, Any]      =       [
              '[UNK]',
              '[CLS]',
              '[SEP]',
              '[PAD]',
              '[MASK]',
              'test',
              'question',
              'this',
              'is',
              'the',
              'first',
              'second',
              'third',
              'fourth',
              'fifth',
              'record',
              'want',
              '##want',
              '##ed',
              'wa',
              'un',
              'runn',
              '##ing',
              ',',
              'low',
              'lowest',
          ]
          __A						: Union[str, Any]      =       os.path.join(self.tmpdirname						,	'realm_tokenizer')
          os.makedirs(_UpperCAmelCase						,	exist_ok=_UpperCAmelCase)
          __A						: Tuple      =       os.path.join(_UpperCAmelCase						,	VOCAB_FILES_NAMES['vocab_file'])
          with open(self.vocab_file						,	'w'						,	encoding='utf-8') as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))

          __A						: int      =       os.path.join(self.tmpdirname						,	'realm_block_records')
          os.makedirs(_UpperCAmelCase						,	exist_ok=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname						,	'realm_tokenizer'))


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       RealmConfig(num_block_records=self.num_block_records)
          return config


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       Dataset.from_dict(
              {
                  'id': ['0', '1'],
                  'question': ['foo', 'bar'],
                  'answers': [['Foo', 'Bar'], ['Bar']],
              })
          return dataset


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       np.array(
              [
                  B'This is the first record',
                  B'This is the second record',
                  B'This is the third record',
                  B'This is the fourth record',
                  B'This is the fifth record',
                  B'This is a longer longer longer record',
              ]						,	dtype=_UpperCAmelCase						,	)
          return block_records


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       RealmRetriever(
              block_records=self.get_dummy_block_records()						,	tokenizer=self.get_tokenizer()						,	)
          return retriever


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_config()
          __A						: str      =       self.get_dummy_retriever()
          __A						: List[str]      =       retriever.tokenizer

          __A						: Dict      =       np.array([0, 3]						,	dtype='long')
          __A						: Dict      =       tokenizer(['Test question']).input_ids
          __A						: Optional[Any]      =       tokenizer(
              ['the fourth']						,	add_special_tokens=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	).input_ids
          __A						: str      =       config.reader_seq_len

          __A ,__A ,__A ,__A						: List[str]      =       retriever(
              _UpperCAmelCase						,	_UpperCAmelCase						,	answer_ids=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	return_tensors='np')

          self.assertEqual(len(_UpperCAmelCase)						,	2)
          self.assertEqual(len(_UpperCAmelCase)						,	2)
          self.assertEqual(len(_UpperCAmelCase)						,	2)
          self.assertEqual(concat_inputs.input_ids.shape						,	(2, 10))
          self.assertEqual(concat_inputs.attention_mask.shape						,	(2, 10))
          self.assertEqual(concat_inputs.token_type_ids.shape						,	(2, 10))
          self.assertEqual(concat_inputs.special_tokens_mask.shape						,	(2, 10))
          self.assertEqual(
              tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0])						,	['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'first', 'record', '[SEP]']						,	)
          self.assertEqual(
              tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1])						,	['[CLS]', 'test', 'question', '[SEP]', 'this', 'is', 'the', 'fourth', 'record', '[SEP]']						,	)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: int      =       self.get_config()
          __A						: Any      =       self.get_dummy_retriever()
          __A						: str      =       retriever.tokenizer

          __A						: Dict      =       np.array([0, 3, 5]						,	dtype='long')
          __A						: Tuple      =       tokenizer(['Test question']).input_ids
          __A						: Union[str, Any]      =       tokenizer(
              ['the fourth', 'longer longer']						,	add_special_tokens=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	).input_ids
          __A						: Dict      =       config.reader_seq_len

          __A ,__A ,__A ,__A						: str      =       retriever(
              _UpperCAmelCase						,	_UpperCAmelCase						,	answer_ids=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	return_tensors='np')

          self.assertEqual([False, True, True]						,	_UpperCAmelCase)
          self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]						,	_UpperCAmelCase)
          self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       self.get_dummy_retriever()
          retriever.save_pretrained(os.path.join(self.tmpdirname						,	'realm_block_records'))

          # Test local path
          __A						: str      =       retriever.from_pretrained(os.path.join(self.tmpdirname						,	'realm_block_records'))
          self.assertEqual(retriever.block_records[0]						,	B'This is the first record')

          # Test mocked remote path
          with patch('transformers.models.realm.retrieval_realm.hf_hub_download') as mock_hf_hub_download:
               __A						: int      =       os.path.join(
                   os.path.join(self.tmpdirname						,	'realm_block_records')						,	_REALM_BLOCK_RECORDS_FILENAME)
               __A						: Tuple      =       RealmRetriever.from_pretrained('google/realm-cc-news-pretrained-openqa')

          self.assertEqual(retriever.block_records[0]						,	B'This is the first record')