



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

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

   if "prime_prior" in key:
      _A      =       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      =       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      =       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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:str	,   _snake_case	:Any	,   _snake_case	:Optional[int]				)  ->     Optional[Any]:
   _A      =       {}
   import re

   _A      =       re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''				)
   _A      =       re.compile(
       r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''				)
   _A      =       re.compile(r'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''				)

   _A      =       re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''				)
   _A      =       re.compile(
       r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''				)
   _A      =       re.compile(r'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''				)

   _A      =       re.compile(r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'''				)
   _A      =       re.compile(
       r'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''				)
   _A      =       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      =       re_encoder_block_conv_in.match(_snake_case				)
         _A      =       regex_match.groups()
         _A      =       int(groups[2]				) * 2 + int(groups[3]				)
         _A      =       F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
         _A      =       re_encoder_block_conv_in.sub(_snake_case	,   _snake_case				)

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

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

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

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

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

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

      elif re_prior_cond_proj_in.fullmatch(_snake_case				):
         _A      =       re_prior_cond_proj_in.match(_snake_case				)
         _A      =       regex_match.groups()
         _A      =       F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
         _A      =       re_prior_cond_proj_in.sub(_snake_case	,   _snake_case				)

      # keep original key
      else:
         _A      =       original_key

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

      _A      =       original_key
      _A      =       value

   return new_dict

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]=None	,   _snake_case	:Union[str, Any]=None				)  ->     Dict:
   for file in MODEL_MAPPING[model_name]:
      if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/'				)[-1]}'''				):
         _A      =       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      =       MODEL_MAPPING[model_name.split('''/'''				)[-1]]

   _A      =       JukeboxConfig.from_pretrained(_snake_case				)
   _A      =       JukeboxModel(_snake_case				)

   _A      =       []
   _A      =       {}
   for i, dict_name in enumerate(_snake_case				):
      _A      =       torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split('/'				)[-1]}'''				)['''model''']

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

      _A      =       '''vqvae''' if i == 0 else F'''priors.{3 - i}'''
      _A      =       fix_jukebox_keys(_snake_case	,   model.state_dict()	,   _snake_case	,   _snake_case				)
      weight_dict.append(_snake_case				)

   _A      =       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_		 =					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)










import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	BioGptTokenizer
    lowerCamelCase__    :		List[Any]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__											=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']     )
        with open(self.vocab_file   ,				'''w'''     ) as fp:
            fp.write(json.dumps(A_     )     )
        with open(self.merges_file   ,				'''w'''     ) as fp:
            fp.write('''\n'''.join(A_     )     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        return input_text, output_text



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer(self.vocab_file   ,				self.merges_file     )

        SCREAMING_SNAKE_CASE__											=			'''lower'''
        SCREAMING_SNAKE_CASE__											=			['''low''', '''er</w>''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + ['''<unk>''']
        SCREAMING_SNAKE_CASE__											=			[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''sequence builders'''   ,				add_special_tokens=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''multi-sequence build'''   ,				add_special_tokens=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_   ,				A_     )

        self.assertTrue(encoded_sentence == [2] + text     )
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a     )
