


"""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()
__magic_name__						: str    =				logging.get_logger(__name__)


__magic_name__						: Dict    =				'https://openaipublic.azureedge.net/jukebox/models/'
__magic_name__						: 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       a_ (	lowercase__ :str     ):
			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       a_ (	lowercase__ :Any,			lowercase__ :Dict,			lowercase__ :List[Any],			lowercase__ :Optional[Any]     ):
			__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(UpperCamelCase__     ):
									__lowerCamelCase          =      re_encoder_block_conv_in.match(UpperCamelCase__     )
									__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(UpperCamelCase__,			UpperCamelCase__     )

						elif re_encoder_block_resnet.fullmatch(UpperCamelCase__     ):
									__lowerCamelCase          =      re_encoder_block_resnet.match(UpperCamelCase__     )
									__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(UpperCamelCase__,			UpperCamelCase__     )

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

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

						elif re_decoder_block_resnet.fullmatch(UpperCamelCase__     ):
									__lowerCamelCase          =      re_decoder_block_resnet.match(UpperCamelCase__     )
									__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(UpperCamelCase__,			UpperCamelCase__     )

						elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__     ):
									__lowerCamelCase          =      re_decoder_block_proj_in.match(UpperCamelCase__     )
									__lowerCamelCase          =      regex_match.groups()
									__lowerCamelCase          =      f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
									__lowerCamelCase          =      re_decoder_block_proj_in.sub(UpperCamelCase__,			UpperCamelCase__     )

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

						elif re_prior_cond_resnet.fullmatch(UpperCamelCase__     ):
									__lowerCamelCase          =      re_prior_cond_resnet.match(UpperCamelCase__     )
									__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(UpperCamelCase__,			UpperCamelCase__     )

						elif re_prior_cond_proj_in.fullmatch(UpperCamelCase__     ):
									__lowerCamelCase          =      re_prior_cond_proj_in.match(UpperCamelCase__     )
									__lowerCamelCase          =      regex_match.groups()
									__lowerCamelCase          =      f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
									__lowerCamelCase          =      re_prior_cond_proj_in.sub(UpperCamelCase__,			UpperCamelCase__     )

						# keep original key
						else:
									__lowerCamelCase          =      original_key

						__lowerCamelCase          =      replace_key(UpperCamelCase__     )

						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       a_ (	lowercase__ :Union[str, Any]=None,			lowercase__ :Any=None     ):
			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=UpperCamelCase__     )
									os.makedirs(f'{pytorch_dump_folder_path}/',			exist_ok=UpperCamelCase__     )
									open(f'{pytorch_dump_folder_path}/{file.split("/"     )[-1]}',			"""wb"""     ).write(r.content     )

			__lowerCamelCase          =      MODEL_MAPPING[model_name.split("""/"""     )[-1]]

			__lowerCamelCase          =      JukeboxConfig.from_pretrained(UpperCamelCase__     )
			__lowerCamelCase          =      JukeboxModel(UpperCamelCase__     )

			__lowerCamelCase          =      []
			__lowerCamelCase          =      {}
			for i, dict_name in enumerate(UpperCamelCase__     ):
						__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(UpperCamelCase__,			model.state_dict(),			UpperCamelCase__,			UpperCamelCase__     )
						weight_dict.append(UpperCamelCase__     )

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

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

			print(f'Saving model {model_name} to {pytorch_dump_folder_path}'     )
			model.save_pretrained(UpperCamelCase__     )

			return weight_dict


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








'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

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


UpperCAmelCase_			:  Union[str, Any]										=   {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase_			:  List[Any]										=   {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

UpperCAmelCase_			:  str										=   {'facebook/blenderbot-3B': 1_2_8}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    VOCAB_FILES_NAMES
 __lowercase					:		List[Any]     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		Optional[Any]     				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase					:		Tuple     				=    ['''input_ids''', '''attention_mask''']
 __lowercase					:		str     				=    BlenderbotTokenizer


 def __init__(   self   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase="replace"   ,   __lowercase="<s>"   ,   __lowercase="</s>"   ,   __lowercase="</s>"   ,   __lowercase="<s>"   ,   __lowercase="<unk>"   ,   __lowercase="<pad>"   ,   __lowercase="<mask>"   ,   __lowercase=False   ,   __lowercase=True   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(
      __lowercase   ,   __lowercase   ,   tokenizer_file=__lowercase   ,   errors=__lowercase   ,   bos_token=__lowercase   ,   eos_token=__lowercase   ,   sep_token=__lowercase   ,   cls_token=__lowercase   ,   unk_token=__lowercase   ,   pad_token=__lowercase   ,   mask_token=__lowercase   ,   add_prefix_space=__lowercase   ,   trim_offsets=__lowercase   ,   **__lowercase   ,   )

  __A	:		str	  =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'   ,   __lowercase    ) != add_prefix_space:
   __A	:		List[Any]	  =      getattr(__lowercase   ,   pre_tok_state.pop('type'    )    )
   __A	:		Any	  =      add_prefix_space
   __A	:		Any	  =      pre_tok_class(**__lowercase    )

  __A	:		Union[str, Any]	  =      add_prefix_space

  __A	:		List[Any]	  =      'post_processor'
  __A	:		Optional[Any]	  =      getattr(self.backend_tokenizer   ,   __lowercase   ,   __lowercase    )
  if tokenizer_component_instance:
   __A	:		Optional[int]	  =      json.loads(tokenizer_component_instance.__getstate__()    )

   # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
   if "sep" in state:
    __A	:		Union[str, Any]	  =      tuple(state['sep']    )
   if "cls" in state:
    __A	:		int	  =      tuple(state['cls']    )

   __A	:		Any	  =      False

   if state.get('add_prefix_space'   ,   __lowercase    ) != add_prefix_space:
    __A	:		Any	  =      add_prefix_space
    __A	:		int	  =      True

   if state.get('trim_offsets'   ,   __lowercase    ) != trim_offsets:
    __A	:		List[Any]	  =      trim_offsets
    __A	:		List[str]	  =      True

   if changes_to_apply:
    __A	:		Optional[int]	  =      getattr(__lowercase   ,   state.pop('type'    )    )
    __A	:		Optional[int]	  =      component_class(**__lowercase    )
    setattr(self.backend_tokenizer   ,   __lowercase   ,   __lowercase    )


 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 			snake_case__ (   self    ):

  """simple docstring"""







  if self._mask_token is None:
   if self.verbose:
    logger.error('Using mask_token, but it is not set yet.'    )
   return None
  return str(self._mask_token    )


 @mask_token.setter
 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      AddedToken(__lowercase   ,   lstrip=__lowercase   ,   rstrip=__lowercase    ) if isinstance(__lowercase   ,   __lowercase    ) else value
  __A	:		List[str]	  =      value


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      kwargs.get('is_split_into_words'   ,   __lowercase    )
  assert self.add_prefix_space or not is_split_into_words, (
      F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
      "to use it with pretokenized inputs."
  )

  return super()._batch_encode_plus(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      kwargs.get('is_split_into_words'   ,   __lowercase    )

  assert self.add_prefix_space or not is_split_into_words, (
      F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
      "to use it with pretokenized inputs."
  )

  return super()._encode_plus(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		str	  =      self._tokenizer.model.save(__lowercase   ,   name=__lowercase    )
  return tuple(__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		str	  =      [self.sep_token_id]
  __A	:		Any	  =      [self.cls_token_id]

  if token_ids_a is None:
   return len(cls + token_ids_a + sep    ) * [0]
  return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  return token_ids_a + [self.eos_token_id]







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      []
  for is_user, text in conversation.iter_texts():
   if is_user:
    # We need to space prefix as it's being done within blenderbot
    inputs.append(' ' + text    )
   else:
    # Generated responses should contain them already.
    inputs.append(__lowercase    )

  __A	:		Tuple	  =      '  '.join(__lowercase    )
  __A	:		List[Any]	  =      self.encode(__lowercase    )
  if len(__lowercase    ) > self.model_max_length:
   __A	:		Any	  =      input_ids[-self.model_max_length :]
   logger.warning(F"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""    )
  return input_ids






