





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 :			Any     		=    logging.get_logger(__name__)


lowercase :			int     		=    "https://openaipublic.azureedge.net/jukebox/models/"
lowercase :			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 A_      (						A__							)    ->						List[Any]:
       if key.endswith('.model.1.bias'							) and len(key.split('.'							)							) > 10:
              a__		:      str					=							key.replace('.model.1.bias'						, '.conv1d_1.bias'							)
       elif key.endswith('.model.1.weight'							) and len(key.split('.'							)							) > 10:
              a__		:      List[Any]					=							key.replace('.model.1.weight'						, '.conv1d_1.weight'							)
       elif key.endswith('.model.3.bias'							) and len(key.split('.'							)							) > 10:
              a__		:      str					=							key.replace('.model.3.bias'						, '.conv1d_2.bias'							)
       elif key.endswith('.model.3.weight'							) and len(key.split('.'							)							) > 10:
              a__		:      Tuple					=							key.replace('.model.3.weight'						, '.conv1d_2.weight'							)

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

       if "prime_prior" in key:
              a__		:      str					=							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__		:      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__		:      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 A_      (						A__						, A__						, A__						, A__							)    ->						Tuple:
       a__		:      Any					=							{}
       import re

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

       a__		:      List[Any]					=							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__		:      str					=							re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'							)

       a__		:      List[Any]					=							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__		:      str					=							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							):
                     a__		:      Any					=							re_encoder_block_conv_in.match(_lowerCamelCase							)
                     a__		:      Optional[int]					=							regex_match.groups()
                     a__		:      Tuple					=							int(groups[2]							) * 2 + int(groups[3]							)
                     a__		:      Optional[int]					=							F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
                     a__		:      List[str]					=							re_encoder_block_conv_in.sub(_lowerCamelCase						, _lowerCamelCase							)

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

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

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

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

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

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

              elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase							):
                     a__		:      Optional[Any]					=							re_prior_cond_proj_in.match(_lowerCamelCase							)
                     a__		:      Union[str, Any]					=							regex_match.groups()
                     a__		:      Dict					=							F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
                     a__		:      str					=							re_prior_cond_proj_in.sub(_lowerCamelCase						, _lowerCamelCase							)

              # keep original key
              else:
                     a__		:      Any					=							original_key

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

              a__		:      Optional[int]					=							original_key
              a__		:      Optional[Any]					=							value

       return new_dict



@torch.no_grad()
def A_      (						A__=None						, A__=None							)    ->						Tuple:
       for file in MODEL_MAPPING[model_name]:
              if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"							)[-1]}'							):
                     a__		:      Any					=							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							)

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

       a__		:      Optional[Any]					=							JukeboxConfig.from_pretrained(_lowerCamelCase							)
       a__		:      Any					=							JukeboxModel(_lowerCamelCase							)

       a__		:      Optional[int]					=							[]
       a__		:      Tuple					=							{}
       for i, dict_name in enumerate(_lowerCamelCase							):
              a__		:      Dict					=							torch.load(F'{pytorch_dump_folder_path}/{dict_name.split("/"							)[-1]}'							)['model']

              a__		:      Optional[Any]					=							{}
              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__		:      Tuple					=							old_dic[k]

              a__		:      Tuple					=							'vqvae' if i == 0 else F'priors.{3 - i}'
              a__		:      int					=							fix_jukebox_keys(_lowerCamelCase						, model.state_dict()						, _lowerCamelCase						, _lowerCamelCase							)
              weight_dict.append(_lowerCamelCase							)

       a__		:      List[Any]					=							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__":
    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 :			int     		=    parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers.generation import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )






@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :str				)->				Any:
     A__							=     3
     A__							=     2_50

     A__							=     ids_tensor((batch_size, length)		,			lowercase_				)
     A__							=     torch.ones((batch_size, length)		,			device=lowercase_		,			dtype=torch.float				) / length
     return input_ids, scores
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     StoppingCriteriaList(
         [
             MaxLengthCriteria(max_length=10				),
             MaxTimeCriteria(max_time=0.1				),
         ]				)

     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__							=     MaxLengthCriteria(max_length=10				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				int:
     A__							=     MaxNewTokensCriteria(start_length=5		,			max_new_tokens=5				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)

     A__							=     StoppingCriteriaList([criteria]				)
     self.assertEqual(criteria_list.max_length		,			10				)
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     MaxTimeCriteria(max_time=0.1				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__							=     MaxTimeCriteria(max_time=0.1		,			initial_timestamp=time.time() - 0.2				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)



 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				str:
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			10				)

     with self.assertWarns(lowercase_				):
         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			11				)

     A__							=     validate_stopping_criteria(StoppingCriteriaList()		,			11				)

     self.assertEqual(len(lowercase_				)		,			1				)



