
'''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()
__lowerCAmelCase   =       logging.get_logger(__name__)


__lowerCAmelCase   =       """https://openaipublic.azureedge.net/jukebox/models/"""
__lowerCAmelCase   =       {
    """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    UpperCAmelCase_						(__a :       List[Any]					):






   """simple docstring"""

   if key.endswith('.model.1.bias'					) and len(key.split('.'					)					) > 1_0:
      _a							:      Union[str, Any]     =  key.replace('.model.1.bias'					,      '.conv1d_1.bias'					)
   elif key.endswith('.model.1.weight'					) and len(key.split('.'					)					) > 1_0:
      _a							:      Optional[Any]     =  key.replace('.model.1.weight'					,      '.conv1d_1.weight'					)
   elif key.endswith('.model.3.bias'					) and len(key.split('.'					)					) > 1_0:
      _a							:      str     =  key.replace('.model.3.bias'					,      '.conv1d_2.bias'					)
   elif key.endswith('.model.3.weight'					) and len(key.split('.'					)					) > 1_0:
      _a							:      Optional[Any]     =  key.replace('.model.3.weight'					,      '.conv1d_2.weight'					)

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

   if "prime_prior" in key:
      _a							:      Union[str, 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[str]     =  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							:      str     =  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    UpperCAmelCase_						(__a :       Optional[Any]					,      __a :       str					,      __a :       Any					,      __a :       Optional[int]					):






   """simple docstring"""

   _a							:      Optional[int]     =  {}
   import re

   _a							:      Optional[int]     =  re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'					)
   _a							:      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							:      Optional[Any]     =  re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'					)
   _a							:      Optional[int]     =  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							:      int     =  re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'					)
   _a							:      str     =  re.compile(
       R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'					)
   _a							:      List[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							:      List[str]     =  re_encoder_block_conv_in.match(_snake_case					)
         _a							:      List[str]     =  regex_match.groups()
         _a							:      Optional[int]     =  int(groups[2]					) * 2 + int(groups[3]					)
         _a							:      Any     =  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							:      Optional[Any]     =  regex_match.groups()
         _a							:      Dict     =  int(groups[2]					) * 2 + int(groups[3]					)
         _a							:      Optional[int]     =  {'1': 1, '3': 2}[groups[-2]]
         _a							:      Any     =  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							:      int     =  prefix + resnet_block
         _a							:      Union[str, Any]     =  re_encoder_block_resnet.sub(_snake_case					,      _snake_case					)

      elif re_encoder_block_proj_out.fullmatch(_snake_case					):
         _a							:      str     =  re_encoder_block_proj_out.match(_snake_case					)
         _a							:      str     =  regex_match.groups()
         _a							:      str     =  f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
         _a							:      int     =  re_encoder_block_proj_out.sub(_snake_case					,      _snake_case					)

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

      elif re_decoder_block_resnet.fullmatch(_snake_case					):
         _a							:      Optional[int]     =  re_decoder_block_resnet.match(_snake_case					)
         _a							:      Optional[Any]     =  regex_match.groups()
         _a							:      List[str]     =  int(groups[2]					) * 2 + int(groups[3]					) - 2
         _a							:      Dict     =  {'1': 1, '3': 2}[groups[-2]]
         _a							:      int     =  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							:      List[Any]     =  prefix + resnet_block
         _a							:      Tuple     =  re_decoder_block_resnet.sub(_snake_case					,      _snake_case					)

      elif re_decoder_block_proj_in.fullmatch(_snake_case					):
         _a							:      Dict     =  re_decoder_block_proj_in.match(_snake_case					)
         _a							:      List[str]     =  regex_match.groups()
         _a							:      Tuple     =  f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
         _a							:      Dict     =  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							:      Union[str, Any]     =  re_prior_cond_conv_out.match(_snake_case					)
         _a							:      Tuple     =  regex_match.groups()
         _a							:      Tuple     =  int(groups[1]					) * 2 + int(groups[2]					) - 2
         _a							:      List[Any]     =  f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
         _a							:      Tuple     =  re_prior_cond_conv_out.sub(_snake_case					,      _snake_case					)

      elif re_prior_cond_resnet.fullmatch(_snake_case					):
         _a							:      Dict     =  re_prior_cond_resnet.match(_snake_case					)
         _a							:      List[Any]     =  regex_match.groups()
         _a							:      Union[str, Any]     =  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							:      List[Any]     =  f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
         _a							:      Optional[int]     =  prefix + resnet_block
         _a							:      Any     =  re_prior_cond_resnet.sub(_snake_case					,      _snake_case					)

      elif re_prior_cond_proj_in.fullmatch(_snake_case					):
         _a							:      Dict     =  re_prior_cond_proj_in.match(_snake_case					)
         _a							:      List[Any]     =  regex_match.groups()
         _a							:      Any     =  f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
         _a							:      str     =  re_prior_cond_proj_in.sub(_snake_case					,      _snake_case					)

      # keep original key
      else:
         _a							:      Tuple     =  original_key

      _a							:      List[Any]     =  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							:      int     =  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							:      str     =  original_key
      _a							:      List[str]     =  value

   return new_dict





@torch.no_grad()
def    UpperCAmelCase_						(__a :       Optional[Any]=None					,      __a :       Union[str, Any]=None					):






   """simple docstring"""

   for file in MODEL_MAPPING[model_name]:
      if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'					)[-1]}"""					):
         _a							:      List[str]     =  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							:      int     =  MODEL_MAPPING[model_name.split('/'					)[-1]]

   _a							:      Tuple     =  JukeboxConfig.from_pretrained(_snake_case					)
   _a							:      Union[str, Any]     =  JukeboxModel(_snake_case					)

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

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

      _a							:      List[Any]     =  'vqvae' if i == 0 else f"""priors.{3 - i}"""
      _a							:      Any     =  fix_jukebox_keys(_snake_case					,      model.state_dict()					,      _snake_case					,      _snake_case					)
      weight_dict.append(_snake_case					)

   _a							:      str     =  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__":
  __lowerCAmelCase   =       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.""",
  )
  __lowerCAmelCase   =       parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AlignProcessor, EfficientNetImageProcessor


@require_vision
class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def     snake_case_      (    self					:					Tuple      )   ->	Optional[int]:
    _A      =       tempfile.mkdtemp()

    _A      =       [
        '''[UNK]''',
        '''[CLS]''',
        '''[SEP]''',
        '''[PAD]''',
        '''[MASK]''',
        '''want''',
        '''##want''',
        '''##ed''',
        '''wa''',
        '''un''',
        '''runn''',
        '''##ing''',
        ''',''',
        '''low''',
        '''lowest''',
    ]
    _A      =       os.path.join(self.tmpdirname			,						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      =       {
        '''do_resize''': True,
        '''size''': 20,
        '''do_center_crop''': True,
        '''crop_size''': 18,
        '''do_normalize''': True,
        '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
        '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
    }
    _A      =       os.path.join(self.tmpdirname			,						__lowerCAmelCase      )
    with open(self.image_processor_file			,						'''w'''			,						encoding='''utf-8'''      ) as fp:
       json.dump(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Dict			,						**__lowerCAmelCase					:					int      )   ->	Optional[int]:
    return BertTokenizer.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						**__lowerCAmelCase					:					Optional[Any]      )   ->	Tuple:
    return BertTokenizerFast.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple			,						**__lowerCAmelCase					:					str      )   ->	Union[str, Any]:
    return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    shutil.rmtree(self.tmpdirname      )

 def     snake_case_      (    self					:					int      )   ->	Optional[Any]:
    _A      =       [np.random.randint(2_55			,						size=(3, 30, 4_00)			,						dtype=np.uinta      )]
    _A      =       [Image.fromarray(np.moveaxis(__lowerCAmelCase			,						0			,						-1      )      ) for x in image_inputs]
    return image_inputs

 def     snake_case_      (    self					:					Dict      )   ->	List[str]:
    _A      =       self.get_tokenizer()
    _A      =       self.get_rust_tokenizer()
    _A      =       self.get_image_processor()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    processor_slow.save_pretrained(self.tmpdirname      )
    _A      =       AlignProcessor.from_pretrained(self.tmpdirname			,						use_fast=__lowerCAmelCase      )

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )
    processor_fast.save_pretrained(self.tmpdirname      )
    _A      =       AlignProcessor.from_pretrained(self.tmpdirname      )

    self.assertEqual(processor_slow.tokenizer.get_vocab()			,						tokenizer_slow.get_vocab()      )
    self.assertEqual(processor_fast.tokenizer.get_vocab()			,						tokenizer_fast.get_vocab()      )
    self.assertEqual(tokenizer_slow.get_vocab()			,						tokenizer_fast.get_vocab()      )
    self.assertIsInstance(processor_slow.tokenizer			,						__lowerCAmelCase      )
    self.assertIsInstance(processor_fast.tokenizer			,						__lowerCAmelCase      )

    self.assertEqual(processor_slow.image_processor.to_json_string()			,						image_processor.to_json_string()      )
    self.assertEqual(processor_fast.image_processor.to_json_string()			,						image_processor.to_json_string()      )
    self.assertIsInstance(processor_slow.image_processor			,						__lowerCAmelCase      )
    self.assertIsInstance(processor_fast.image_processor			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]      )   ->	List[str]:
    _A      =       AlignProcessor(tokenizer=self.get_tokenizer()			,						image_processor=self.get_image_processor()      )
    processor.save_pretrained(self.tmpdirname      )

    _A      =       self.get_tokenizer(bos_token='''(BOS)'''			,						eos_token='''(EOS)'''      )
    _A      =       self.get_image_processor(do_normalize=__lowerCAmelCase			,						padding_value=1.0      )

    _A      =       AlignProcessor.from_pretrained(
        self.tmpdirname			,						bos_token='''(BOS)'''			,						eos_token='''(EOS)'''			,						do_normalize=__lowerCAmelCase			,						padding_value=1.0      )

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

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

 def     snake_case_      (    self					:					str      )   ->	List[Any]:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       self.prepare_image_inputs()

    _A      =       image_processor(__lowerCAmelCase			,						return_tensors='''np'''      )
    _A      =       processor(images=__lowerCAmelCase			,						return_tensors='''np'''      )

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

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       '''lower newer'''

    _A      =       processor(text=__lowerCAmelCase      )

    _A      =       tokenizer(__lowerCAmelCase			,						padding='''max_length'''			,						max_length=64      )

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

 def     snake_case_      (    self					:					List[str]      )   ->	Any:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       '''lower newer'''
    _A      =       self.prepare_image_inputs()

    _A      =       processor(text=__lowerCAmelCase			,						images=__lowerCAmelCase      )

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

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

 def     snake_case_      (    self					:					Optional[Any]      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    _A      =       processor.batch_decode(__lowerCAmelCase      )
    _A      =       tokenizer.batch_decode(__lowerCAmelCase      )

    self.assertListEqual(__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					str      )   ->	str:
    _A      =       self.get_image_processor()
    _A      =       self.get_tokenizer()

    _A      =       AlignProcessor(tokenizer=__lowerCAmelCase			,						image_processor=__lowerCAmelCase      )

    _A      =       '''lower newer'''
    _A      =       self.prepare_image_inputs()

    _A      =       processor(text=__lowerCAmelCase			,						images=__lowerCAmelCase      )

    self.assertListEqual(list(inputs.keys()      )			,						processor.model_input_names      )



