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()
A     :  Any					     =       logging.get_logger(__name__)


A     :  Union[str, Any]					     =       "https://openaipublic.azureedge.net/jukebox/models/"
A     :  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      a__ (		__UpperCamelCase				):
      if key.endswith(".model.1.bias"				) and len(key.split("."				)				) > 1_0:
            SCREAMING_SNAKE_CASE_          =  key.replace(".model.1.bias"     ,      ".conv1d_1.bias"				)
      elif key.endswith(".model.1.weight"				) and len(key.split("."				)				) > 1_0:
            SCREAMING_SNAKE_CASE_          =  key.replace(".model.1.weight"     ,      ".conv1d_1.weight"				)
      elif key.endswith(".model.3.bias"				) and len(key.split("."				)				) > 1_0:
            SCREAMING_SNAKE_CASE_          =  key.replace(".model.3.bias"     ,      ".conv1d_2.bias"				)
      elif key.endswith(".model.3.weight"				) and len(key.split("."				)				) > 1_0:
            SCREAMING_SNAKE_CASE_          =  key.replace(".model.3.weight"     ,      ".conv1d_2.weight"				)

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

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

      if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
            SCREAMING_SNAKE_CASE_          =  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:
            SCREAMING_SNAKE_CASE_          =  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      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  {}
      import re

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

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

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

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

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

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

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

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

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

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

            elif re_prior_cond_proj_in.fullmatch(__UpperCamelCase				):
                  SCREAMING_SNAKE_CASE_          =  re_prior_cond_proj_in.match(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  regex_match.groups()
                  SCREAMING_SNAKE_CASE_          =  F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
                  SCREAMING_SNAKE_CASE_          =  re_prior_cond_proj_in.sub(__UpperCamelCase     ,      __UpperCamelCase				)

            # keep original key
            else:
                  SCREAMING_SNAKE_CASE_          =  original_key

            SCREAMING_SNAKE_CASE_          =  replace_key(__UpperCamelCase				)

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

            SCREAMING_SNAKE_CASE_          =  original_key
            SCREAMING_SNAKE_CASE_          =  value

      return new_dict


@torch.no_grad()
def      a__ (		__UpperCamelCase=None     ,      __UpperCamelCase=None				):
      for file in MODEL_MAPPING[model_name]:
            if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'''				):
                  SCREAMING_SNAKE_CASE_          =  requests.get(F'''{PREFIX}{file}'''     ,      allow_redirects=__UpperCamelCase				)
                  os.makedirs(F'''{pytorch_dump_folder_path}/'''     ,      exist_ok=__UpperCamelCase				)
                  open(F'''{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'''     ,      "wb"				).write(r.content				)

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

      SCREAMING_SNAKE_CASE_          =  JukeboxConfig.from_pretrained(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  JukeboxModel(__UpperCamelCase				)

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

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

            SCREAMING_SNAKE_CASE_          =  "vqvae" if i == 0 else F'''priors.{3 - i}'''
            SCREAMING_SNAKE_CASE_          =  fix_jukebox_keys(__UpperCamelCase     ,      model.state_dict()     ,      __UpperCamelCase     ,      __UpperCamelCase				)
            weight_dict.append(__UpperCamelCase				)

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

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

      print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
      model.save_pretrained(__UpperCamelCase				)

      return weight_dict


if __name__ == "__main__":
      A     :  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.",
      )
      A     :  Optional[int]					     =       parser.parse_args()
      convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Dict   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-base"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 768)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )







  @slow
  def    __A     (							self :       Tuple   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  XLMRobertaModel.from_pretrained("xlm-roberta-large"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]   )
        # The dog is cute and lives in the garden house

        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 12, 1_024)   )  # batch_size, sequence_length, embedding_vector_dim
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]   )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )["last_hidden_state"].detach()
        self.assertEqual(output.shape  ,     __magic_name__   )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]  ,     __magic_name__  ,     atol=1e-3   )   )


