



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase						    =  get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")

@require_sentencepiece
@require_tokenizers
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			PegasusTokenizer
     lowerCAmelCase					=			PegasusTokenizerFast
     lowerCAmelCase					=			True
     lowerCAmelCase					=			True





     def 	__snake_case    (       self :							List[Any]				)->Union[str, Any]:
      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	PegasusTokenizer(UpperCamelCase				)
      tokenizer.save_pretrained(self.tmpdirname				)





     @cached_property
     def 	__snake_case    (       self :							Union[str, Any]				)->Any:
      return PegasusTokenizer.from_pretrained("google/pegasus-large"				)





     def 	__snake_case    (       self :							Optional[int]     ,  **UpperCamelCase :							str				)->PegasusTokenizer:
      return PegasusTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							int     ,  UpperCamelCase :							int				)->List[str]:
      return ("This is a test", "This is a test")





     def 	__snake_case    (       self :							Any				)->Any:
      __SCREAMING_SNAKE_CASE       :    Dict       =	"</s>"
      __SCREAMING_SNAKE_CASE       :    Tuple       =	1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase				)     ,  UpperCamelCase				)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase				)     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							Any				)->str:
      __SCREAMING_SNAKE_CASE       :    Any       =	list(self.get_tokenizer().get_vocab().keys()				)

      self.assertEqual(vocab_keys[0]     ,  "<pad>"				)
      self.assertEqual(vocab_keys[1]     ,  "</s>"				)
      self.assertEqual(vocab_keys[-1]     ,  "v"				)
      self.assertEqual(len(UpperCamelCase				)     ,  1_1_0_3				)





     def 	__snake_case    (       self :							List[Any]				)->Dict:
      self.assertEqual(self.get_tokenizer().vocab_size     ,  1_1_0_3				)





     def 	__snake_case    (       self :							str				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.tokenizer_class.from_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    int       =	(
          "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
          " </s> <pad> <pad> <pad>"
      )
      __SCREAMING_SNAKE_CASE       :    str       =	rust_tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase     ,  add_special_tokens=UpperCamelCase				).input_ids[0]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	py_tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase     ,  add_special_tokens=UpperCamelCase				).input_ids[0]
      self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							int				)->List[Any]:
      __SCREAMING_SNAKE_CASE       :    Dict       =	self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      __SCREAMING_SNAKE_CASE       :    List[str]       =	"<mask_1> To ensure a <mask_2> flow of bank resolutions."
      __SCREAMING_SNAKE_CASE       :    Tuple       =	[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      __SCREAMING_SNAKE_CASE       :    Any       =	tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase				).input_ids[0]
      self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    int       =	self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 9_6_1_0_3
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 1_0_3
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1_0_2_4
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	"To ensure a smooth flow of bank resolutions."
      __SCREAMING_SNAKE_CASE       :    Any       =	[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase				).input_ids[0]
      self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]				) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





     @require_torch
     def 	__snake_case    (       self :							str				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Any       =	["This is going to be way too long." * 1_5_0, "short example"]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	["not super long but more than 5 tokens", "tiny"]
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self._large_tokenizer(UpperCamelCase     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  return_tensors="pt"				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self._large_tokenizer(
          text_target=UpperCamelCase     ,  max_length=5     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  return_tensors="pt"				)

      assert batch.input_ids.shape == (2, 1_0_2_4)
      assert batch.attention_mask.shape == (2, 1_0_2_4)
      assert targets["input_ids"].shape == (2, 5)
      assert len(UpperCamelCase				) == 2  # input_ids, attention_mask.






     @slow
     def 	__snake_case    (       self :							List[str]				)->Optional[Any]:
      # fmt: off
      __SCREAMING_SNAKE_CASE       :    Dict       =	{"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=UpperCamelCase     ,  model_name="google/bigbird-pegasus-large-arxiv"     ,  revision="ba85d0851d708441f91440d509690f1ab6353415"     ,  )






@require_sentencepiece
@require_tokenizers
class       _SCREAMING_SNAKE_CASE	(UpperCamelCase			,    unittest.TestCase ):
     lowerCAmelCase					=			PegasusTokenizer
     lowerCAmelCase					=			PegasusTokenizerFast
     lowerCAmelCase					=			True
     lowerCAmelCase					=			True





     def 	__snake_case    (       self :							Tuple				)->Dict:
      super().setUp()

      # We have a SentencePiece fixture for testing
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	PegasusTokenizer(UpperCamelCase     ,  offset=0     ,  mask_token_sent=UpperCamelCase     ,  mask_token="[MASK]"				)
      tokenizer.save_pretrained(self.tmpdirname				)





     @cached_property
     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"				)





     def 	__snake_case    (       self :							Optional[int]     ,  **UpperCamelCase :							List[str]				)->PegasusTokenizer:
      return PegasusTokenizer.from_pretrained(self.tmpdirname     ,  **UpperCamelCase				)





     def 	__snake_case    (       self :							Any     ,  UpperCamelCase :							Union[str, Any]				)->List[Any]:
      return ("This is a test", "This is a test")





     def 	__snake_case    (       self :							List[str]				)->Any:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    Any       =	self.tokenizer_class.from_pretrained(self.tmpdirname				)
      __SCREAMING_SNAKE_CASE       :    Tuple       =	(
          "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
          " <pad> <pad> <pad>"
      )
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	rust_tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase     ,  add_special_tokens=UpperCamelCase				).input_ids[0]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	py_tokenizer([raw_input_str]     ,  return_tensors=UpperCamelCase     ,  add_special_tokens=UpperCamelCase				).input_ids[0]
      self.assertListEqual(UpperCamelCase     ,  UpperCamelCase				)





     @require_torch
     def 	__snake_case    (       self :							Tuple				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	["This is going to be way too long." * 1_0_0_0, "short example"]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	["not super long but more than 5 tokens", "tiny"]
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self._large_tokenizer(UpperCamelCase     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  return_tensors="pt"				)
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self._large_tokenizer(
          text_target=UpperCamelCase     ,  max_length=5     ,  padding=UpperCamelCase     ,  truncation=UpperCamelCase     ,  return_tensors="pt"				)

      assert batch.input_ids.shape == (2, 4_0_9_6)
      assert batch.attention_mask.shape == (2, 4_0_9_6)
      assert targets["input_ids"].shape == (2, 5)
      assert len(UpperCamelCase				) == 2  # input_ids, attention_mask.






     def 	__snake_case    (       self :							List[Any]				)->Tuple:
      __SCREAMING_SNAKE_CASE       :    int       =	(
          "This is an example string that is used to test the original TF implementation against the HF"
          " implementation"
      )

      __SCREAMING_SNAKE_CASE       :    Tuple       =	self._large_tokenizer(UpperCamelCase				).input_ids

      self.assertListEqual(
          UpperCamelCase     ,  [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]     ,  )











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   :     Any     ):



 """simple docstring"""





 if key.endswith(".model.1.bias"     ) and len(key.split("."     )     ) > 10:
  __SCREAMING_SNAKE_CASE       :    Dict       =	key.replace(".model.1.bias"		,	".conv1d_1.bias"     )
 elif key.endswith(".model.1.weight"     ) and len(key.split("."     )     ) > 10:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	key.replace(".model.1.weight"		,	".conv1d_1.weight"     )
 elif key.endswith(".model.3.bias"     ) and len(key.split("."     )     ) > 10:
  __SCREAMING_SNAKE_CASE       :    Dict       =	key.replace(".model.3.bias"		,	".conv1d_2.bias"     )
 elif key.endswith(".model.3.weight"     ) and len(key.split("."     )     ) > 10:
  __SCREAMING_SNAKE_CASE       :    List[str]       =	key.replace(".model.3.weight"		,	".conv1d_2.weight"     )

 if "conditioner_blocks.0." in key:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	key.replace("conditioner_blocks.0"		,	"conditioner_blocks"     )

 if "prime_prior" in key:
  __SCREAMING_SNAKE_CASE       :    Dict       =	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       :    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:
  __SCREAMING_SNAKE_CASE       :    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   :     Tuple		,	__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Optional[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	{}
 import re

 __SCREAMING_SNAKE_CASE       :    str       =	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	re.compile(
     r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"     )

 __SCREAMING_SNAKE_CASE       :    int       =	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	re.compile(
     r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    List[str]       =	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"     )

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

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

  elif re_encoder_block_proj_out.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Any       =	re_encoder_block_proj_out.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    int       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    Dict       =	re_encoder_block_proj_out.sub(__lowerCamelCase		,	__lowerCamelCase     )

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

  elif re_decoder_block_resnet.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	re_decoder_block_resnet.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    int       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(groups[2]     ) * 2 + int(groups[3]     ) - 2
   __SCREAMING_SNAKE_CASE       :    Any       =	{"1": 1, "3": 2}[groups[-2]]
   __SCREAMING_SNAKE_CASE       :    Tuple       =	F"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	prefix + resnet_block
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	re_decoder_block_resnet.sub(__lowerCamelCase		,	__lowerCamelCase     )

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

  elif re_prior_cond_resnet.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	re_prior_cond_resnet.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    Any       =	int(groups[1]     ) * 2 + int(groups[2]     ) - 2
   __SCREAMING_SNAKE_CASE       :    str       =	{"1": 1, "3": 2}[groups[-2]]
   __SCREAMING_SNAKE_CASE       :    Any       =	F"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	F"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    Any       =	prefix + resnet_block
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	re_prior_cond_resnet.sub(__lowerCamelCase		,	__lowerCamelCase     )

  elif re_prior_cond_proj_in.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Tuple       =	re_prior_cond_proj_in.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Dict       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    Dict       =	F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    int       =	re_prior_cond_proj_in.sub(__lowerCamelCase		,	__lowerCamelCase     )

  # keep original key
  else:
   __SCREAMING_SNAKE_CASE       :    Tuple       =	original_key

  __SCREAMING_SNAKE_CASE       :    Union[str, 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:
   __SCREAMING_SNAKE_CASE       :    List[str]       =	model_state_dict[F"""{key_prefix}.{key}"""]
   print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""     )
   __SCREAMING_SNAKE_CASE       :    str       =	original_key

  __SCREAMING_SNAKE_CASE       :    List[str]       =	original_key
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	value

 return new_dict






@torch.no_grad()
def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]=None		,	__lowerCamelCase   :     List[Any]=None     ):



 """simple docstring"""





 for file in MODEL_MAPPING[model_name]:
  if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"     )[-1]}"""     ):
   __SCREAMING_SNAKE_CASE       :    Dict       =	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     )

 __SCREAMING_SNAKE_CASE       :    int       =	MODEL_MAPPING[model_name.split("/"     )[-1]]

 __SCREAMING_SNAKE_CASE       :    List[str]       =	JukeboxConfig.from_pretrained(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    str       =	JukeboxModel(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    int       =	[]
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{}
 for i, dict_name in enumerate(__lowerCamelCase     ):
  __SCREAMING_SNAKE_CASE       :    str       =	torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/"     )[-1]}"""     )["model"]

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

  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"vqvae" if i == 0 else F"""priors.{3 - i}"""
  __SCREAMING_SNAKE_CASE       :    int       =	fix_jukebox_keys(__lowerCamelCase		,	model.state_dict()		,	__lowerCamelCase		,	__lowerCamelCase     )
  weight_dict.append(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Dict       =	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)






