


'''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				:  int    		=      logging.get_logger(__name__)


lowerCamelCase				:  str    		=      'https://openaipublic.azureedge.net/jukebox/models/'
lowerCamelCase				:  Optional[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 _SCREAMING_SNAKE_CASE (A			)			->			Optional[int]:






       """simple docstring"""







       if key.endswith('''.model.1.bias'''			) and len(key.split('''.'''			)			) > 10:
              lowercase__      =					key.replace('''.model.1.bias'''    ,					'''.conv1d_1.bias'''			)
       elif key.endswith('''.model.1.weight'''			) and len(key.split('''.'''			)			) > 10:
              lowercase__      =					key.replace('''.model.1.weight'''    ,					'''.conv1d_1.weight'''			)
       elif key.endswith('''.model.3.bias'''			) and len(key.split('''.'''			)			) > 10:
              lowercase__      =					key.replace('''.model.3.bias'''    ,					'''.conv1d_2.bias'''			)
       elif key.endswith('''.model.3.weight'''			) and len(key.split('''.'''			)			) > 10:
              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 _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A			)			->			Tuple:






       """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(A			):
                     lowercase__      =					re_encoder_block_conv_in.match(A			)
                     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(A    ,					A			)

              elif re_encoder_block_resnet.fullmatch(A			):
                     lowercase__      =					re_encoder_block_resnet.match(A			)
                     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(A    ,					A			)

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

              # rename vqvae.decoder keys
              elif re_decoder_block_conv_out.fullmatch(A			):
                     lowercase__      =					re_decoder_block_conv_out.match(A			)
                     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(A    ,					A			)

              elif re_decoder_block_resnet.fullmatch(A			):
                     lowercase__      =					re_decoder_block_resnet.match(A			)
                     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(A    ,					A			)

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

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

              elif re_prior_cond_resnet.fullmatch(A			):
                     lowercase__      =					re_prior_cond_resnet.match(A			)
                     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(A    ,					A			)

              elif re_prior_cond_proj_in.fullmatch(A			):
                     lowercase__      =					re_prior_cond_proj_in.match(A			)
                     lowercase__      =					regex_match.groups()
                     lowercase__      =					f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
                     lowercase__      =					re_prior_cond_proj_in.sub(A    ,					A			)

              # keep original key
              else:
                     lowercase__      =					original_key

              lowercase__      =					replace_key(A			)

              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 _SCREAMING_SNAKE_CASE (A=None    ,					A=None			)			->			Tuple:






       """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=A			)
                     os.makedirs(f"{pytorch_dump_folder_path}/"    ,					exist_ok=A			)
                     open(f"{pytorch_dump_folder_path}/{file.split('/'			)[-1]}"    ,					'''wb'''			).write(r.content			)

       lowercase__      =					MODEL_MAPPING[model_name.split('''/'''			)[-1]]

       lowercase__      =					JukeboxConfig.from_pretrained(A			)
       lowercase__      =					JukeboxModel(A			)

       lowercase__      =					[]
       lowercase__      =					{}
       for i, dict_name in enumerate(A			):
              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(A    ,					model.state_dict()    ,					A    ,					A			)
              weight_dict.append(A			)

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

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

       print(f"Saving model {model_name} to {pytorch_dump_folder_path}"			)
       model.save_pretrained(A			)

       return weight_dict


if __name__ == "__main__":
 lowerCamelCase				:  str    		=      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 argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def _SCREAMING_SNAKE_CASE (A			)			->			Dict:






       """simple docstring"""







       lowercase__      =					os.path.join(args.tf_model_dir    ,					'''parameters.json'''			)
       lowercase__      =					json.loads(open(A			).read()			)
       if not params:
              raise ValueError(
                  f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."			)
       if not args.output.endswith('''.pt'''			):
              lowercase__      =					args.output + '''.pt'''
       lowercase__      =					OrderedDict()
       with tf.device('''/CPU:0'''			):
              lowercase__      =					tf.train.load_checkpoint(args.tf_model_dir			)
              lowercase__      =					reader.get_variable_to_shape_map()
              for key_name in shapes.keys():
                     lowercase__      =					reader.get_tensor(A			).astype(np.floataa			)
                     if key_name.endswith('''/adam_m'''			) or key_name.endswith('''/adam_v'''			):
                            continue
                     if key_name.startswith('''pasts/'''			):
                            if key_name.startswith('''pasts/mlp'''			):
                                   lowercase__      =					int(key_name[9]			)
                            elif key_name.startswith('''pasts/out'''			):
                                   lowercase__      =					8
                            lowercase__      =					'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                            lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/moe'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/switch_gating/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/softmlp/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/wo/kernel'''			) or key_name.endswith('''/wi/kernel'''			):
                                   lowercase__      =					key_name[-9:-7]
                                   for i in range(16			):
                                          lowercase__      =					'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
                                          lowercase__      =					(
                                              vnp[i].transpose([1, 0]			).copy()
                                          )  # In Mesh-Tensorflow, it is one array, so it is divided
                                          lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/mlp'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/p1/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p1/bias'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p2/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p2/bias'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/ln'''			):
                            lowercase__      =					int(key_name[8:].split('''/'''			)[0]			)
                            if key_name.endswith('''/b'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.norm.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/g'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.norm.weight''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/att'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/qkv/kernel'''			):
                                   lowercase__      =					vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                                   lowercase__      =					state[:, 0, :, :]
                                   lowercase__      =					state[:, 1, :, :]
                                   lowercase__      =					state[:, 2, :, :]
                                   lowercase__      =					(
                                       state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
                                       .transpose([1, 0]			)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					(
                                       state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
                                       .transpose([1, 0]			)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					(
                                       state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
                                       .transpose([1, 0]			)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/o/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
                                   lowercase__      =					(
                                       vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/an'''			):
                            lowercase__      =					int(key_name[8:].split('''/'''			)[0]			)
                            if key_name.endswith('''/b'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.norm.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/g'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.norm.weight''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif (
                         key_name.startswith('''model/wte'''			)
                         or key_name.startswith('''model/wpe'''			)
                         or key_name.startswith('''model/ete'''			)
                     ):
                            lowercase__      =					{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
                                key_name[-3:]
                            ]
                            lowercase__      =					'''model.%s.weight''' % nlayer
                            lowercase__      =					vnp.copy()  # same in embedded
                            lowercase__      =					torch.tensor(A			)
                            if key_name.startswith('''model/wte'''			):
                                   lowercase__      =					'''lm_head.weight'''
                                   lowercase__      =					vnp.copy()  # same in embedded
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/wob'''			):
                            lowercase__      =					'''final_logits_bias'''
                            lowercase__      =					vnp.copy()  # same in embedded
                            lowercase__      =					state.reshape((1, -1)			)
                            lowercase__      =					torch.tensor(A			)
                     elif key_name == "model/dense/kernel":
                            lowercase__      =					'''model.last_project.weight'''
                            lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowercase__      =					torch.tensor(A			)
                     elif key_name == "model/dense_1/bias":
                            lowercase__      =					'''model.last_project.bias'''
                            lowercase__      =					vnp.copy()  # same because it is one dimensional
                            lowercase__      =					torch.tensor(A			)
       torch.save(A    ,					args.output			)


if __name__ == "__main__":
 lowerCamelCase				:  Optional[int]    		=      argparse.ArgumentParser(
     description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
 )
 parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
 parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
 lowerCamelCase				:  Dict    		=      parser.parse_args()
 convert_tf_gptsan_to_pt(args)




