"""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__     						=  logging.get_logger(__name__)


UpperCAmelCase__     						=  """https://openaipublic.azureedge.net/jukebox/models/"""
UpperCAmelCase__     						=  {
    """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    (	lowercase						):







  """simple docstring"""
  if key.endswith(""".model.1.bias"""						) and len(key.split("""."""						)						) > 10:
    _UpperCAmelCase         =					key.replace(""".model.1.bias"""      ,""".conv1d_1.bias"""						)
  elif key.endswith(""".model.1.weight"""						) and len(key.split("""."""						)						) > 10:
    _UpperCAmelCase         =					key.replace(""".model.1.weight"""      ,""".conv1d_1.weight"""						)
  elif key.endswith(""".model.3.bias"""						) and len(key.split("""."""						)						) > 10:
    _UpperCAmelCase         =					key.replace(""".model.3.bias"""      ,""".conv1d_2.bias"""						)
  elif key.endswith(""".model.3.weight"""						) and len(key.split("""."""						)						) > 10:
    _UpperCAmelCase         =					key.replace(""".model.3.weight"""      ,""".conv1d_2.weight"""						)

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

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

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    _UpperCAmelCase         =					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:
    _UpperCAmelCase         =					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    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					{}
  import re

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

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

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

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

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

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

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

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

    # rename prior cond.model to upsampler.upsample_block and resnet
    elif re_prior_cond_conv_out.fullmatch(lowercase						):
      _UpperCAmelCase         =					re_prior_cond_conv_out.match(lowercase						)
      _UpperCAmelCase         =					regex_match.groups()
      _UpperCAmelCase         =					int(groups[1]						) * 2 + int(groups[2]						) - 2
      _UpperCAmelCase         =					f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
      _UpperCAmelCase         =					re_prior_cond_conv_out.sub(lowercase      ,lowercase						)

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

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

    # keep original key
    else:
      _UpperCAmelCase         =					original_key

    _UpperCAmelCase         =					replace_key(lowercase						)

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

    _UpperCAmelCase         =					original_key
    _UpperCAmelCase         =					value

  return new_dict






@torch.no_grad()
def 			__UpperCAmelCase    (	lowercase=None      ,lowercase=None						):







  """simple docstring"""
  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"						)[-1]}'''						):
      _UpperCAmelCase         =					requests.get(f'''{PREFIX}{file}'''      ,allow_redirects=lowercase						)
      os.makedirs(f'''{pytorch_dump_folder_path}/'''      ,exist_ok=lowercase						)
      open(f'''{pytorch_dump_folder_path}/{file.split("/"						)[-1]}'''      ,"""wb"""						).write(r.content						)

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

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

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

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

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

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

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

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

  return weight_dict


if __name__ == "__main__":
    UpperCAmelCase__     						=  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__     						=  parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)

"""simple docstring"""


from sklearn.metrics import recall_score

import datasets


UpperCAmelCase__     						=  """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


UpperCAmelCase__     						=  """
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


UpperCAmelCase__     						=  """
@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}
"""


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


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return datasets.MetricInfo(
          description=_DESCRIPTION      , citation=_CITATION      , inputs_description=_KWARGS_DESCRIPTION      , features=datasets.Features(
              {
                  """predictions""": datasets.Sequence(datasets.Value("""int32"""    )    ),
                  """references""": datasets.Sequence(datasets.Value("""int32"""    )    ),
              }
              if self.config_name == """multilabel"""
              else {
                  """predictions""": datasets.Value("""int32"""    ),
                  """references""": datasets.Value("""int32"""    ),
              }    )      , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html"""]      , )


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Optional[Any]      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     Dict=1      , __lowerCAmelCase		:     List[str]="binary"      , __lowerCAmelCase		:     Any=None      , __lowerCAmelCase		:     int="warn"      , ):
      _UpperCAmelCase         =					recall_score(
          __lowerCAmelCase      , __lowerCAmelCase      , labels=__lowerCAmelCase      , pos_label=__lowerCAmelCase      , average=__lowerCAmelCase      , sample_weight=__lowerCAmelCase      , zero_division=__lowerCAmelCase      , )
      return {"recall": float(__lowerCAmelCase    ) if score.size == 1 else score}
