

"""simple docstring"""






import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    None
    if token is not None:
        _lowerCAmelCase       :   Union[str, Any]  =    {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

    _lowerCAmelCase       :   List[str]  =    f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    _lowerCAmelCase       :   List[Any]  =    requests.get(_lowerCamelCase ,   headers=_lowerCamelCase   ).json()
    _lowerCAmelCase       :   int  =    {}

    try:
        job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )
        _lowerCAmelCase       :   Union[str, Any]  =    math.ceil((result['total_count'] - 100) / 100   )

        for i in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   List[Any]  =    requests.get(url + f"""&page={i + 2}""" ,   headers=_lowerCamelCase   ).json()
            job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )

        return job_links
    except Exception:
        print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    None
    if token is not None:
        _lowerCAmelCase       :   List[str]  =    {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

    _lowerCAmelCase       :   Optional[Any]  =    f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    _lowerCAmelCase       :   List[Any]  =    requests.get(_lowerCamelCase ,   headers=_lowerCamelCase   ).json()
    _lowerCAmelCase       :   Union[str, Any]  =    {}

    try:
        artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )
        _lowerCAmelCase       :   int  =    math.ceil((result['total_count'] - 100) / 100   )

        for i in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   str  =    requests.get(url + f"""&page={i + 2}""" ,   headers=_lowerCamelCase   ).json()
            artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )

        return artifacts
    except Exception:
        print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    None
    if token is not None:
        _lowerCAmelCase       :   List[Any]  =    {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

    _lowerCAmelCase       :   Union[str, Any]  =    requests.get(_lowerCamelCase ,   headers=_lowerCamelCase ,   allow_redirects=_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    result.headers['Location']
    _lowerCAmelCase       :   Optional[Any]  =    requests.get(_lowerCamelCase ,   allow_redirects=_lowerCamelCase   )
    _lowerCAmelCase       :   Any  =    os.path.join(_lowerCamelCase ,   f"""{artifact_name}.zip"""   )
    with open(_lowerCamelCase ,   'wb'   ) as fp:
        fp.write(response.content   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    []
    _lowerCAmelCase       :   Dict  =    []
    _lowerCAmelCase       :   Union[str, Any]  =    None

    with zipfile.ZipFile(_lowerCamelCase   ) as z:
        for filename in z.namelist():
            if not os.path.isdir(_lowerCamelCase   ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(_lowerCamelCase   ) as f:
                        for line in f:
                            _lowerCAmelCase       :   Any  =    line.decode('UTF-8'   ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _lowerCAmelCase       :   Any  =    line[: line.index(': '   )]
                                    _lowerCAmelCase       :   List[str]  =    line[line.index(': '   ) + len(': '   ) :]
                                    errors.append([error_line, error]   )
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith('FAILED '   ):
                                # `test` is the test method that failed
                                _lowerCAmelCase       :   List[Any]  =    line[len('FAILED '   ) :]
                                failed_tests.append(_lowerCamelCase   )
                            elif filename == "job_name.txt":
                                _lowerCAmelCase       :   List[str]  =    line

    if len(_lowerCamelCase   ) != len(_lowerCamelCase   ):
        raise ValueError(
            f"""`errors` and `failed_tests` should have the same number of elements. Got {len(_lowerCamelCase   )} for `errors` """
            f"""and {len(_lowerCamelCase   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
            ' problem.'   )

    _lowerCAmelCase       :   Dict  =    None
    if job_name and job_links:
        _lowerCAmelCase       :   Tuple  =    job_links.get(_lowerCamelCase ,   _lowerCamelCase   )

    # A list with elements of the form (line of error, error, failed test)
    _lowerCAmelCase       :   List[str]  =    [x + [y] + [job_link] for x, y in zip(_lowerCamelCase ,   _lowerCamelCase   )]

    return result

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []

    _lowerCAmelCase       :   int  =    [os.path.join(_lowerCamelCase ,   _lowerCamelCase   ) for p in os.listdir(_lowerCamelCase   ) if p.endswith('.zip'   )]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(_lowerCamelCase ,   job_links=_lowerCamelCase   )   )

    return errors

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    Counter()
    counter.update([x[1] for x in logs]   )
    _lowerCAmelCase       :   Optional[Any]  =    counter.most_common()
    _lowerCAmelCase       :   Optional[int]  =    {}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _lowerCAmelCase       :   Optional[int]  =    {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

    _lowerCAmelCase       :   List[Any]  =    dict(sorted(r.items() ,   key=lambda _lowerCamelCase   : item[1]["count"] ,   reverse=_lowerCamelCase   )   )
    return r

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    test.split('::'   )[0]
    if test.startswith('tests/models/'   ):
        _lowerCAmelCase       :   List[str]  =    test.split('/'   )[2]
    else:
        _lowerCAmelCase       :   Tuple  =    None

    return test

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    [(x[0], x[1], get_model(x[2]   )) for x in logs]
    _lowerCAmelCase       :   List[Any]  =    [x for x in logs if x[2] is not None]
    _lowerCAmelCase       :   Optional[Any]  =    {x[2] for x in logs}

    _lowerCAmelCase       :   Optional[Any]  =    {}
    for test in tests:
        _lowerCAmelCase       :   Any  =    Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]   )
        _lowerCAmelCase       :   List[str]  =    counter.most_common()
        _lowerCAmelCase       :   str  =    {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        _lowerCAmelCase       :   Dict  =    sum(error_counts.values()   )
        if n_errors > 0:
            _lowerCAmelCase       :   Union[str, Any]  =    {'count': n_errors, 'errors': error_counts}

    _lowerCAmelCase       :   Optional[Any]  =    dict(sorted(r.items() ,   key=lambda _lowerCamelCase   : item[1]["count"] ,   reverse=_lowerCamelCase   )   )
    return r

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    '| no. | error | status |'
    _lowerCAmelCase       :   Any  =    '|-:|:-|:-|'
    _lowerCAmelCase       :   Dict  =    [header, sep]
    for error in reduced_by_error:
        _lowerCAmelCase       :   Optional[Any]  =    reduced_by_error[error]['count']
        _lowerCAmelCase       :   Any  =    f"""| {count} | {error[:100]} |  |"""
        lines.append(_lowerCamelCase   )

    return "\n".join(_lowerCamelCase   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    '| model | no. of errors | major error | count |'
    _lowerCAmelCase       :   Dict  =    '|-:|-:|-:|-:|'
    _lowerCAmelCase       :   str  =    [header, sep]
    for model in reduced_by_model:
        _lowerCAmelCase       :   Dict  =    reduced_by_model[model]['count']
        _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    list(reduced_by_model[model]['errors'].items()   )[0]
        _lowerCAmelCase       :   Union[str, Any]  =    f"""| {model} | {count} | {error[:60]} | {_count} |"""
        lines.append(_lowerCamelCase   )

    return "\n".join(_lowerCamelCase   )


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
 parser.add_argument(
     """--output_dir""",
     type=str,
     required=True,
     help="""Where to store the downloaded artifacts and other result files.""",
 )
 parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
 _lowerCAmelCase						=		parser.parse_args()

 os.makedirs(args.output_dir, exist_ok=True)

 _lowerCAmelCase						=		get_job_links(args.workflow_run_id, token=args.token)
 _lowerCAmelCase						=		{}
 # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
 # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
 if _job_links:
  for k, v in _job_links.items():
   # This is how GitHub actions combine job names.
   if " / " in k:
    _lowerCAmelCase						=		k.find(""" / """)
    _lowerCAmelCase						=		k[index + len(""" / """) :]
   _lowerCAmelCase						=		v
 with open(os.path.join(args.output_dir, """job_links.json"""), """w""", encoding="""UTF-8""") as fp:
  json.dump(job_links, fp, ensure_ascii=False, indent=4)

 _lowerCAmelCase						=		get_artifacts_links(args.workflow_run_id, token=args.token)
 with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

 for idx, (name, url) in enumerate(artifacts.items()):
  download_artifact(name, url, args.output_dir, args.token)
  # Be gentle to GitHub
  time.sleep(1)

 _lowerCAmelCase						=		get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 _lowerCAmelCase						=		Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 _lowerCAmelCase						=		counter.most_common(3_0)
 for item in most_common:
  print(item)

 with open(os.path.join(args.output_dir, """errors.json"""), """w""", encoding="""UTF-8""") as fp:
  json.dump(errors, fp, ensure_ascii=False, indent=4)

 _lowerCAmelCase						=		reduce_by_error(errors)
 _lowerCAmelCase						=		reduce_by_model(errors)

 _lowerCAmelCase						=		make_github_table(reduced_by_error)
 _lowerCAmelCase						=		make_github_table_per_model(reduced_by_model)

 with open(os.path.join(args.output_dir, """reduced_by_error.txt"""), """w""", encoding="""UTF-8""") as fp:
  fp.write(sa)
 with open(os.path.join(args.output_dir, """reduced_by_model.txt"""), """w""", encoding="""UTF-8""") as fp:
  fp.write(sa)








"""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 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if key.endswith('.model.1.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.bias' ,   '.conv1d_1.bias'   )
    elif key.endswith('.model.1.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.weight' ,   '.conv1d_1.weight'   )
    elif key.endswith('.model.3.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   int  =    key.replace('.model.3.bias' ,   '.conv1d_2.bias'   )
    elif key.endswith('.model.3.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Tuple  =    key.replace('.model.3.weight' ,   '.conv1d_2.weight'   )

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

    if "prime_prior" in key:
        _lowerCAmelCase       :   str  =    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       :   Optional[Any]  =    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       :   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 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    {}
    import re

    _lowerCAmelCase       :   Optional[Any]  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Optional[int]  =    re.compile(
        R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Dict  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Union[str, Any]  =    re.compile(
        R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

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

        elif re_encoder_block_resnet.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_encoder_block_resnet.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Union[str, Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    int(groups[2]   ) * 2 + int(groups[3]   )
            _lowerCAmelCase       :   Tuple  =    {'1': 1, '3': 2}[groups[-2]]
            _lowerCAmelCase       :   Union[str, Any]  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
            _lowerCAmelCase       :   Optional[int]  =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Optional[int]  =    prefix + resnet_block
            _lowerCAmelCase       :   Dict  =    re_encoder_block_resnet.sub(_lowerCamelCase ,   _lowerCamelCase   )

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

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

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

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

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

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

        # keep original key
        else:
            _lowerCAmelCase       :   Optional[Any]  =    original_key

        _lowerCAmelCase       :   List[Any]  =    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:
            _lowerCAmelCase       :   Dict  =    model_state_dict[f"""{key_prefix}.{key}"""]
            print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""   )
            _lowerCAmelCase       :   Optional[int]  =    original_key

        _lowerCAmelCase       :   Union[str, Any]  =    original_key
        _lowerCAmelCase       :   Optional[Any]  =    value

    return new_dict

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase=None ,   _lowerCamelCase=None   ):

    '''simple docstring'''



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

    _lowerCAmelCase       :   Union[str, Any]  =    MODEL_MAPPING[model_name.split('/'   )[-1]]

    _lowerCAmelCase       :   Optional[Any]  =    JukeboxConfig.from_pretrained(_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    JukeboxModel(_lowerCamelCase   )

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

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

        _lowerCAmelCase       :   List[str]  =    'vqvae' if i == 0 else f"""priors.{3 - i}"""
        _lowerCAmelCase       :   Tuple  =    fix_jukebox_keys(_lowerCamelCase ,   model.state_dict() ,   _lowerCamelCase ,   _lowerCamelCase   )
        weight_dict.append(_lowerCamelCase   )

    _lowerCAmelCase       :   List[Any]  =    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						=		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)





