'''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()
A_     :						int    							=logging.get_logger(__name__)


A_     :						str    							='https://openaipublic.azureedge.net/jukebox/models/'
A_     :						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 snake_case_      (					__snake_case  :							Any)			->					Optional[int]:







   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_      (					__snake_case  :							str	,   __snake_case  :							Optional[int]	,   __snake_case  :							Tuple	,   __snake_case  :							Any)			->					Tuple:
   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(__snake_case):
         lowerCAmelCase_			=      re_encoder_block_conv_in.match(__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(__snake_case	,   __snake_case)

      elif re_encoder_block_resnet.fullmatch(__snake_case):
         lowerCAmelCase_			=      re_encoder_block_resnet.match(__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(__snake_case	,   __snake_case)

      elif re_encoder_block_proj_out.fullmatch(__snake_case):
         lowerCAmelCase_			=      re_encoder_block_proj_out.match(__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(__snake_case	,   __snake_case)

      # rename vqvae.decoder keys
      elif re_decoder_block_conv_out.fullmatch(__snake_case):
         lowerCAmelCase_			=      re_decoder_block_conv_out.match(__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(__snake_case	,   __snake_case)

      elif re_decoder_block_resnet.fullmatch(__snake_case):
         lowerCAmelCase_			=      re_decoder_block_resnet.match(__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(__snake_case	,   __snake_case)

      elif re_decoder_block_proj_in.fullmatch(__snake_case):
         lowerCAmelCase_			=      re_decoder_block_proj_in.match(__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(__snake_case	,   __snake_case)

      # rename prior cond.model to upsampler.upsample_block and resnet
      elif re_prior_cond_conv_out.fullmatch(__snake_case):
         lowerCAmelCase_			=      re_prior_cond_conv_out.match(__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(__snake_case	,   __snake_case)

      elif re_prior_cond_resnet.fullmatch(__snake_case):
         lowerCAmelCase_			=      re_prior_cond_resnet.match(__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(__snake_case	,   __snake_case)

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

      # keep original key
      else:
         lowerCAmelCase_			=      original_key

      lowerCAmelCase_			=      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:
         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_      (					__snake_case  :							List[Any]=None	,   __snake_case  :							List[str]=None)			->					Tuple:







   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=__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)

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

   lowerCAmelCase_			=      JukeboxConfig.from_pretrained(__snake_case)
   lowerCAmelCase_			=      JukeboxModel(__snake_case)

   lowerCAmelCase_			=      []
   lowerCAmelCase_			=      {}
   for i, dict_name in enumerate(__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(__snake_case	,   model.state_dict()	,   __snake_case	,   __snake_case)
      weight_dict.append(__snake_case)

   lowerCAmelCase_			=      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__":
 A_     :						str    							=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.''',
 )
 A_     :						str    							=parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''

import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class     __lowerCAmelCase		(lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	str       =				RoFormerTokenizer
      lowerCAmelCase__   :	Dict       =				RoFormerTokenizerFast
      lowerCAmelCase__   :	List[str]       =				True
      lowerCAmelCase__   :	List[str]       =				True



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             super().setUp()



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       int					):

             '''simple docstring'''







             return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					'''永和服装饰品有限公司,今天天气非常好'''
             lowercase__      =					'''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
             return input_text, output_text



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					self.get_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)

             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)

             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.get_rust_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)
             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)
             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             pass






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             pass




