
'''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()
lowercase			:				Optional[Any]    		=			logging.get_logger(__name__)


lowercase			:				Dict    		=			'https://openaipublic.azureedge.net/jukebox/models/'
lowercase			:				Any    		=			{
    '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_				(   snake_case__						):




    '''simple docstring'''



    if key.endswith('''.model.1.bias'''						) and len(key.split('''.'''						)						) > 10:
        A       :   Dict					=						key.replace('''.model.1.bias'''		,				'''.conv1d_1.bias'''						)
    elif key.endswith('''.model.1.weight'''						) and len(key.split('''.'''						)						) > 10:
        A       :   Union[str, Any]					=						key.replace('''.model.1.weight'''		,				'''.conv1d_1.weight'''						)
    elif key.endswith('''.model.3.bias'''						) and len(key.split('''.'''						)						) > 10:
        A       :   int					=						key.replace('''.model.3.bias'''		,				'''.conv1d_2.bias'''						)
    elif key.endswith('''.model.3.weight'''						) and len(key.split('''.'''						)						) > 10:
        A       :   List[str]					=						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       :   List[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:
        A       :   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:
        A       :   List[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_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   str					=						{}
    import re

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

    A       :   Any					=						re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''						)
    A       :   int					=						re.compile(
        R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''						)
    A       :   Any					=						re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''						)

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

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

        elif re_encoder_block_proj_out.fullmatch(snake_case__						):
            A       :   Optional[Any]					=						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       :   Dict					=						re_encoder_block_proj_out.sub(snake_case__		,				snake_case__						)

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(snake_case__						):
            A       :   Dict					=						re_decoder_block_conv_out.match(snake_case__						)
            A       :   int					=						regex_match.groups()
            A       :   Union[str, Any]					=						int(groups[2]						) * 2 + int(groups[3]						) - 2
            A       :   List[str]					=						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       :   List[Any]					=						re_decoder_block_resnet.match(snake_case__						)
            A       :   Any					=						regex_match.groups()
            A       :   int					=						int(groups[2]						) * 2 + int(groups[3]						) - 2
            A       :   int					=						{'''1''': 1, '''3''': 2}[groups[-2]]
            A       :   Optional[Any]					=						F'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
            A       :   Union[str, Any]					=						F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            A       :   str					=						prefix + resnet_block
            A       :   Optional[Any]					=						re_decoder_block_resnet.sub(snake_case__		,				snake_case__						)

        elif re_decoder_block_proj_in.fullmatch(snake_case__						):
            A       :   Optional[int]					=						re_decoder_block_proj_in.match(snake_case__						)
            A       :   Tuple					=						regex_match.groups()
            A       :   Dict					=						F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
            A       :   List[Any]					=						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       :   List[Any]					=						re_prior_cond_conv_out.match(snake_case__						)
            A       :   List[str]					=						regex_match.groups()
            A       :   str					=						int(groups[1]						) * 2 + int(groups[2]						) - 2
            A       :   Tuple					=						F'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
            A       :   Optional[Any]					=						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       :   Union[str, Any]					=						regex_match.groups()
            A       :   Union[str, Any]					=						int(groups[1]						) * 2 + int(groups[2]						) - 2
            A       :   List[Any]					=						{'''1''': 1, '''3''': 2}[groups[-2]]
            A       :   Any					=						F'conditioner_blocks.upsampler.upsample_block.{block_index}.'
            A       :   List[Any]					=						F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
            A       :   Any					=						prefix + resnet_block
            A       :   str					=						re_prior_cond_resnet.sub(snake_case__		,				snake_case__						)

        elif re_prior_cond_proj_in.fullmatch(snake_case__						):
            A       :   Optional[Any]					=						re_prior_cond_proj_in.match(snake_case__						)
            A       :   Tuple					=						regex_match.groups()
            A       :   List[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       :   Optional[Any]					=						original_key

        A       :   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       :   Dict					=						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       :   Optional[int]					=						value

    return new_dict







@torch.no_grad()
def 		lowerCAmelCase_				(   snake_case__=None		,				snake_case__=None						):




    '''simple docstring'''



    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"						)[-1]}'						):
            A       :   int					=						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       :   List[Any]					=						JukeboxConfig.from_pretrained(snake_case__						)
    A       :   Optional[Any]					=						JukeboxModel(snake_case__						)

    A       :   Optional[Any]					=						[]
    A       :   Tuple					=						{}
    for i, dict_name in enumerate(snake_case__						):
        A       :   Union[str, 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       :   Optional[int]					=						old_dic[k]
            elif k.endswith('''.w'''						):
                A       :   int					=						old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                A       :   Dict					=						old_dic[k]
            else:
                A       :   int					=						old_dic[k]

        A       :   List[Any]					=						'''vqvae''' if i == 0 else F'priors.{3 - i}'
        A       :   Tuple					=						fix_jukebox_keys(snake_case__		,				model.state_dict()		,				snake_case__		,				snake_case__						)
        weight_dict.append(snake_case__						)

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






'''simple docstring'''




import requests
from bsa import BeautifulSoup







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   str					=						BeautifulSoup(requests.get(snake_case__		,				params=snake_case__						).content		,				'''html.parser'''						)
    A       :   Dict					=						soup.find('''div'''		,				attrs={'''class''': '''gs_ri'''}						)
    A       :   Optional[int]					=						div.find('''div'''		,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
    return anchors[2].get_text()


if __name__ == "__main__":
      lowercase			:				str    		=			{
          'title': (
              'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
              'capacitance, volumetric capacitance, and energy density'
          ),
          'journal': 'Chem. Mater.',
          'volume': 30,
          'pages': '3979-3990',
          'year': 20_18,
          'hl': 'en',
      }
      print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




