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


__lowerCAmelCase					: Dict        ="""https://openaipublic.azureedge.net/jukebox/models/"""
__lowerCAmelCase					: Optional[int]        ={
    """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__ (       lowerCAmelCase__  :Optional[Any]	)				->					Dict:
      '''simple docstring'''


      if key.endswith(""".model.1.bias"""	) and len(key.split("""."""	)	) > 1_0:
            lowercase														=			key.replace(""".model.1.bias"""      ,       """.conv1d_1.bias"""	)
      elif key.endswith(""".model.1.weight"""	) and len(key.split("""."""	)	) > 1_0:
            lowercase														=			key.replace(""".model.1.weight"""      ,       """.conv1d_1.weight"""	)
      elif key.endswith(""".model.3.bias"""	) and len(key.split("""."""	)	) > 1_0:
            lowercase														=			key.replace(""".model.3.bias"""      ,       """.conv1d_2.bias"""	)
      elif key.endswith(""".model.3.weight"""	) and len(key.split("""."""	)	) > 1_0:
            lowercase														=			key.replace(""".model.3.weight"""      ,       """.conv1d_2.weight"""	)

      if "conditioner_blocks.0." in key:
            lowercase														=			key.replace("""conditioner_blocks.0"""      ,       """conditioner_blocks"""	)

      if "prime_prior" in key:
            lowercase														=			key.replace("""prime_prior"""      ,       """encoder"""	)

      if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
            lowercase														=			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:
            lowercase														=			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__ (       lowerCAmelCase__  :Tuple      ,       lowerCAmelCase__  :Optional[int]      ,       lowerCAmelCase__  :Tuple      ,       lowerCAmelCase__  :Tuple	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			{}
      import re

      lowercase														=			re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""	)
      lowercase														=			re.compile(
          R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""	)
      lowercase														=			re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""	)

      lowercase														=			re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""	)
      lowercase														=			re.compile(
          R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""	)
      lowercase														=			re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""	)

      lowercase														=			re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""	)
      lowercase														=			re.compile(
          R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""	)
      lowercase														=			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__	):
                  lowercase														=			re_encoder_block_conv_in.match(lowerCAmelCase__	)
                  lowercase														=			regex_match.groups()
                  lowercase														=			int(groups[2]	) * 2 + int(groups[3]	)
                  lowercase														=			f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
                  lowercase														=			re_encoder_block_conv_in.sub(lowerCAmelCase__      ,       lowerCAmelCase__	)

            elif re_encoder_block_resnet.fullmatch(lowerCAmelCase__	):
                  lowercase														=			re_encoder_block_resnet.match(lowerCAmelCase__	)
                  lowercase														=			regex_match.groups()
                  lowercase														=			int(groups[2]	) * 2 + int(groups[3]	)
                  lowercase														=			{"""1""": 1, """3""": 2}[groups[-2]]
                  lowercase														=			f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
                  lowercase														=			f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
                  lowercase														=			prefix + resnet_block
                  lowercase														=			re_encoder_block_resnet.sub(lowerCAmelCase__      ,       lowerCAmelCase__	)

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

            # rename vqvae.decoder keys
            elif re_decoder_block_conv_out.fullmatch(lowerCAmelCase__	):
                  lowercase														=			re_decoder_block_conv_out.match(lowerCAmelCase__	)
                  lowercase														=			regex_match.groups()
                  lowercase														=			int(groups[2]	) * 2 + int(groups[3]	) - 2
                  lowercase														=			f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
                  lowercase														=			re_decoder_block_conv_out.sub(lowerCAmelCase__      ,       lowerCAmelCase__	)

            elif re_decoder_block_resnet.fullmatch(lowerCAmelCase__	):
                  lowercase														=			re_decoder_block_resnet.match(lowerCAmelCase__	)
                  lowercase														=			regex_match.groups()
                  lowercase														=			int(groups[2]	) * 2 + int(groups[3]	) - 2
                  lowercase														=			{"""1""": 1, """3""": 2}[groups[-2]]
                  lowercase														=			f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
                  lowercase														=			f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
                  lowercase														=			prefix + resnet_block
                  lowercase														=			re_decoder_block_resnet.sub(lowerCAmelCase__      ,       lowerCAmelCase__	)

            elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase__	):
                  lowercase														=			re_decoder_block_proj_in.match(lowerCAmelCase__	)
                  lowercase														=			regex_match.groups()
                  lowercase														=			f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
                  lowercase														=			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__	):
                  lowercase														=			re_prior_cond_conv_out.match(lowerCAmelCase__	)
                  lowercase														=			regex_match.groups()
                  lowercase														=			int(groups[1]	) * 2 + int(groups[2]	) - 2
                  lowercase														=			f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
                  lowercase														=			re_prior_cond_conv_out.sub(lowerCAmelCase__      ,       lowerCAmelCase__	)

            elif re_prior_cond_resnet.fullmatch(lowerCAmelCase__	):
                  lowercase														=			re_prior_cond_resnet.match(lowerCAmelCase__	)
                  lowercase														=			regex_match.groups()
                  lowercase														=			int(groups[1]	) * 2 + int(groups[2]	) - 2
                  lowercase														=			{"""1""": 1, """3""": 2}[groups[-2]]
                  lowercase														=			f'conditioner_blocks.upsampler.upsample_block.{block_index}.'
                  lowercase														=			f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
                  lowercase														=			prefix + resnet_block
                  lowercase														=			re_prior_cond_resnet.sub(lowerCAmelCase__      ,       lowerCAmelCase__	)

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

            # keep original key
            else:
                  lowercase														=			original_key

            lowercase														=			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:
                  lowercase														=			model_state_dict[f'{key_prefix}.{key}']
                  print(f'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'	)
                  lowercase														=			original_key

            lowercase														=			original_key
            lowercase														=			value

      return new_dict


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any=None      ,       lowerCAmelCase__  :Dict=None	)				->					Dict:
      '''simple docstring'''


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

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

      lowercase														=			JukeboxConfig.from_pretrained(lowerCAmelCase__	)
      lowercase														=			JukeboxModel(lowerCAmelCase__	)

      lowercase														=			[]
      lowercase														=			{}
      for i, dict_name in enumerate(lowerCAmelCase__	):
            lowercase														=			torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"	)[-1]}'	)["""model"""]

            lowercase														=			{}
            for k in old_dic.keys():
                  if k.endswith(""".b"""	):
                        lowercase														=			old_dic[k]
                  elif k.endswith(""".w"""	):
                        lowercase														=			old_dic[k]
                  elif "level_2" not in dict_name and "cond.model." in k:
                        lowercase														=			old_dic[k]
                  else:
                        lowercase														=			old_dic[k]

            lowercase														=			"""vqvae""" if i == 0 else f'priors.{3 - i}'
            lowercase														=			fix_jukebox_keys(lowerCAmelCase__      ,       model.state_dict()      ,       lowerCAmelCase__      ,       lowerCAmelCase__	)
            weight_dict.append(lowerCAmelCase__	)

      lowercase														=			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__":
       __lowerCAmelCase					: Union[str, Any]        =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					: str        =parser.parse_args()
       convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




"""simple docstring"""






import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__lowerCAmelCase					: str        ="""\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

__lowerCAmelCase					: str        ="""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

__lowerCAmelCase					: Optional[int]        ="""
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION			)
class 				_A		( datasets.Metric			):

      def        A__ (		self		):







            """simple docstring"""



            return datasets.MetricInfo(
                description=_DESCRIPTION	, citation=_CITATION	, inputs_description=_KWARGS_DESCRIPTION	, features=datasets.Features(
                    {
                        """predictions""": datasets.Value("""string"""	, id="""sequence"""		),
                        """references""": datasets.Value("""string"""	, id="""sequence"""		),
                    }		)	, codebase_urls=["""https://github.com/google-research/google-research/tree/master/rouge"""]	, reference_urls=[
                    """https://en.wikipedia.org/wiki/ROUGE_(metric)""",
                    """https://github.com/google-research/google-research/tree/master/rouge""",
                ]	, )







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=False		):







            """simple docstring"""



            if rouge_types is None:
                  lowercase														=			["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

            lowercase														=			rouge_scorer.RougeScorer(rouge_types=__lowerCAmelCase	, use_stemmer=__lowerCAmelCase		)
            if use_aggregator:
                  lowercase														=			scoring.BootstrapAggregator()
            else:
                  lowercase														=			[]

            for ref, pred in zip(__lowerCAmelCase	, __lowerCAmelCase		):
                  lowercase														=			scorer.score(__lowerCAmelCase	, __lowerCAmelCase		)
                  if use_aggregator:
                        aggregator.add_scores(__lowerCAmelCase		)
                  else:
                        scores.append(__lowerCAmelCase		)

            if use_aggregator:
                  lowercase														=			aggregator.aggregate()
            else:
                  lowercase														=			{}
                  for key in scores[0]:
                        lowercase														=			[score[key] for score in scores]

            return result



