


'''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()
UpperCAmelCase_ :     List[str]								=      logging.get_logger(__name__)


UpperCAmelCase_ :     List[Any]								=      'https://openaipublic.azureedge.net/jukebox/models/'
UpperCAmelCase_ :     Tuple								=      {
    '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 			_lowercase       (				UpperCamelCase__       :					Tuple			):
 if key.endswith('.model.1.bias'			) and len(key.split('.'			)			) > 10:
  __A  :		Dict       =			key.replace('.model.1.bias',				'.conv1d_1.bias'			)
 elif key.endswith('.model.1.weight'			) and len(key.split('.'			)			) > 10:
  __A  :		str       =			key.replace('.model.1.weight',				'.conv1d_1.weight'			)
 elif key.endswith('.model.3.bias'			) and len(key.split('.'			)			) > 10:
  __A  :		List[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[Any]       =			key.replace('conditioner_blocks.0',				'conditioner_blocks'			)

 if "prime_prior" in key:
  __A  :		List[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  :		Dict       =			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 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					str			):
 __A  :		List[str]       =			{}
 import re

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

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

  elif re_encoder_block_resnet.fullmatch(__SCREAMING_SNAKE_CASE			):
   __A  :		Union[str, Any]       =			re_encoder_block_resnet.match(__SCREAMING_SNAKE_CASE			)
   __A  :		Optional[Any]       =			regex_match.groups()
   __A  :		Dict       =			int(groups[2]			) * 2 + int(groups[3]			)
   __A  :		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  :		Union[str, Any]       =			prefix + resnet_block
   __A  :		int       =			re_encoder_block_resnet.sub(__SCREAMING_SNAKE_CASE,				__SCREAMING_SNAKE_CASE			)

  elif re_encoder_block_proj_out.fullmatch(__SCREAMING_SNAKE_CASE			):
   __A  :		Optional[int]       =			re_encoder_block_proj_out.match(__SCREAMING_SNAKE_CASE			)
   __A  :		Dict       =			regex_match.groups()
   __A  :		Any       =			f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
   __A  :		str       =			re_encoder_block_proj_out.sub(__SCREAMING_SNAKE_CASE,				__SCREAMING_SNAKE_CASE			)

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

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

  elif re_decoder_block_proj_in.fullmatch(__SCREAMING_SNAKE_CASE			):
   __A  :		Union[str, Any]       =			re_decoder_block_proj_in.match(__SCREAMING_SNAKE_CASE			)
   __A  :		Tuple       =			regex_match.groups()
   __A  :		Optional[int]       =			f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
   __A  :		int       =			re_decoder_block_proj_in.sub(__SCREAMING_SNAKE_CASE,				__SCREAMING_SNAKE_CASE			)

  # rename prior cond.model to upsampler.upsample_block and resnet
  elif re_prior_cond_conv_out.fullmatch(__SCREAMING_SNAKE_CASE			):
   __A  :		int       =			re_prior_cond_conv_out.match(__SCREAMING_SNAKE_CASE			)
   __A  :		Dict       =			regex_match.groups()
   __A  :		Any       =			int(groups[1]			) * 2 + int(groups[2]			) - 2
   __A  :		Any       =			f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
   __A  :		List[str]       =			re_prior_cond_conv_out.sub(__SCREAMING_SNAKE_CASE,				__SCREAMING_SNAKE_CASE			)

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

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

  # keep original key
  else:
   __A  :		Tuple       =			original_key

  __A  :		Tuple       =			replace_key(__SCREAMING_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  :		Tuple       =			model_state_dict[f"""{key_prefix}.{key}"""]
   print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""			)
   __A  :		Optional[Any]       =			original_key

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

 return new_dict


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]=None,				UpperCamelCase__       :					Union[str, Any]=None			):
 for file in MODEL_MAPPING[model_name]:
  if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'			)[-1]}"""			):
   __A  :		Tuple       =			requests.get(f"""{PREFIX}{file}""",				allow_redirects=__SCREAMING_SNAKE_CASE			)
   os.makedirs(f"""{pytorch_dump_folder_path}/""",				exist_ok=__SCREAMING_SNAKE_CASE			)
   open(f"""{pytorch_dump_folder_path}/{file.split('/'			)[-1]}""",				'wb'			).write(r.content			)

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

 __A  :		Optional[int]       =			JukeboxConfig.from_pretrained(__SCREAMING_SNAKE_CASE			)
 __A  :		Tuple       =			JukeboxModel(__SCREAMING_SNAKE_CASE			)

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

  __A  :		List[str]       =			{}
  for k in old_dic.keys():
   if k.endswith('.b'			):
    __A  :		str       =			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  :		Union[str, Any]       =			old_dic[k]
   else:
    __A  :		Any       =			old_dic[k]

  __A  :		str       =			'vqvae' if i == 0 else f"""priors.{3 - i}"""
  __A  :		Optional[int]       =			fix_jukebox_keys(__SCREAMING_SNAKE_CASE,				model.state_dict(),				__SCREAMING_SNAKE_CASE,				__SCREAMING_SNAKE_CASE			)
  weight_dict.append(__SCREAMING_SNAKE_CASE			)

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

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

 print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""			)
 model.save_pretrained(__SCREAMING_SNAKE_CASE			)

 return weight_dict


if __name__ == "__main__":
 UpperCAmelCase_ :     Tuple								=      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_ :     Union[str, Any]								=      parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			tempfile.mkdtemp()

  __A  :		List[Any]       =			BlipImageProcessor()
  __A  :		List[str]       =			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'						)

  __A  :		Union[str, Any]       =			BlipProcessor(__lowercase      ,      __lowercase						)

  processor.save_pretrained(self.tmpdirname						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).tokenizer


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).image_processor


 def  snake_case__   (      self						):

  """simple docstring"""







  shutil.rmtree(self.tmpdirname						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			[np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta						)]

  __A  :		Union[str, Any]       =			[Image.fromarray(np.moveaxis(__lowercase      ,      0      ,      -1						)						) for x in image_inputs]

  return image_inputs


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			BlipProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()						)
  processor.save_pretrained(self.tmpdirname						)

  __A  :		Optional[int]       =			self.get_tokenizer(bos_token='(BOS)'      ,      eos_token='(EOS)'						)
  __A  :		Optional[Any]       =			self.get_image_processor(do_normalize=__lowercase      ,      padding_value=1.0						)

  __A  :		List[str]       =			BlipProcessor.from_pretrained(
      self.tmpdirname      ,      bos_token='(BOS)'      ,      eos_token='(EOS)'      ,      do_normalize=__lowercase      ,      padding_value=1.0						)

  self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()						)
  self.assertIsInstance(processor.tokenizer      ,      __lowercase						)

  self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()						)
  self.assertIsInstance(processor.image_processor      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.get_image_processor()
  __A  :		Union[str, Any]       =			self.get_tokenizer()

  __A  :		Dict       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Dict       =			self.prepare_image_inputs()

  __A  :		str       =			image_processor(__lowercase      ,      return_tensors='np'						)
  __A  :		List[Any]       =			processor(images=__lowercase      ,      return_tensors='np'						)

  for key in input_feat_extract.keys():
   self.assertAlmostEqual(input_feat_extract[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		List[str]       =			self.get_tokenizer()

  __A  :		str       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'

  __A  :		Any       =			processor(text=__lowercase						)

  __A  :		int       =			tokenizer(__lowercase      ,      return_token_type_ids=__lowercase						)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.get_image_processor()
  __A  :		Dict       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		Optional[Any]       =			processor(text=__lowercase      ,      images=__lowercase						)

  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)

  # test if it raises when no input is passed
  with pytest.raises(__lowercase						):
   processor()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		Optional[int]       =			self.get_tokenizer()

  __A  :		List[str]       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		str       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

  __A  :		Tuple       =			processor.batch_decode(__lowercase						)
  __A  :		Tuple       =			tokenizer.batch_decode(__lowercase						)

  self.assertListEqual(__lowercase      ,      __lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_image_processor()
  __A  :		Any       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Optional[int]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		int       =			processor(text=__lowercase      ,      images=__lowercase						)

  # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)






