
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
    import faiss


@require_faiss
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							tempfile.mkdtemp()
       a_      :	Optional[int]      =							8

       # DPR tok
       a_      :	Optional[Any]      =							[
           """[UNK]""",
           """[CLS]""",
           """[SEP]""",
           """[PAD]""",
           """[MASK]""",
           """want""",
           """##want""",
           """##ed""",
           """wa""",
           """un""",
           """runn""",
           """##ing""",
           """,""",
           """low""",
           """lowest""",
       ]
       a_      :	Any      =							os.path.join(self.tmpdirname							,		"""dpr_tokenizer""" )
       os.makedirs(lowerCAmelCase_							,		exist_ok=lowerCAmelCase_ )
       a_      :	str      =							os.path.join(lowerCAmelCase_							,		DPR_VOCAB_FILES_NAMES["""vocab_file"""] )
       with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8""" ) as vocab_writer:
         vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )

       # BART tok
       a_      :	Optional[Any]      =							[
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
       ]
       a_      :	str      =							dict(zip(lowerCAmelCase_							,		range(len(lowerCAmelCase_ ) ) ) )
       a_      :	Any      =							["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       a_      :	Union[str, Any]      =							{"""unk_token""": """<unk>"""}

       a_      :	List[str]      =							os.path.join(self.tmpdirname							,		"""bart_tokenizer""" )
       os.makedirs(lowerCAmelCase_							,		exist_ok=lowerCAmelCase_ )
       a_      :	str      =							os.path.join(lowerCAmelCase_							,		BART_VOCAB_FILES_NAMES["""vocab_file"""] )
       a_      :	Tuple      =							os.path.join(lowerCAmelCase_							,		BART_VOCAB_FILES_NAMES["""merges_file"""] )
       with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8""" ) as fp:
         fp.write(json.dumps(lowerCAmelCase_ ) + """\n""" )
       with open(self.merges_file							,		"""w"""							,		encoding="""utf-8""" ) as fp:
         fp.write("""\n""".join(lowerCAmelCase_ ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,		"""dpr_tokenizer""" ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,		"""dpr_tokenizer""" ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,		"""bart_tokenizer""" ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       shutil.rmtree(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							Dataset.from_dict(
           {
               """id""": ["""0""", """1"""],
               """text""": ["""foo""", """bar"""],
               """title""": ["""Foo""", """Bar"""],
               """embeddings""": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
           } )
       dataset.add_faiss_index("""embeddings"""							,		string_factory="""Flat"""							,		metric_type=faiss.METRIC_INNER_PRODUCT )
       return dataset


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.get_dummy_dataset()
       a_      :	int      =							RagConfig(
           retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		)
       with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
         a_      :	List[str]      =							dataset
         a_      :	Any      =							RagRetriever(
             lowerCAmelCase_							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		)
       return retriever


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							self.get_dummy_dataset()
       a_      :	Tuple      =							RagConfig(
           retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		index_name="""custom"""							,		)
       if from_disk:
         a_      :	Union[str, Any]      =							os.path.join(self.tmpdirname							,		"""dataset""" )
         a_      :	List[str]      =							os.path.join(self.tmpdirname							,		"""index.faiss""" )
         dataset.get_index("""embeddings""" ).save(os.path.join(self.tmpdirname							,		"""index.faiss""" ) )
         dataset.drop_index("""embeddings""" )
         dataset.save_to_disk(os.path.join(self.tmpdirname							,		"""dataset""" ) )
         del dataset
         a_      :	str      =							RagRetriever(
             lowerCAmelCase_							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		)
       else:
         a_      :	Union[str, Any]      =							RagRetriever(
             lowerCAmelCase_							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		index=CustomHFIndex(config.retrieval_vector_size							,		lowerCAmelCase_ )							,		)
       return retriever


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							Dataset.from_dict(
           {
               """id""": ["""0""", """1"""],
               """text""": ["""foo""", """bar"""],
               """title""": ["""Foo""", """Bar"""],
               """embeddings""": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
           } )
       dataset.add_faiss_index("""embeddings"""							,		string_factory="""Flat"""							,		metric_type=faiss.METRIC_INNER_PRODUCT )

       a_      :	int      =							os.path.join(self.tmpdirname							,		"""hf_bert_base.hnswSQ8_correct_phi_128.c_index""" )
       dataset.save_faiss_index("""embeddings"""							,		index_file_name + """.index.dpr""" )
       pickle.dump(dataset["""id"""]							,		open(index_file_name + """.index_meta.dpr"""							,		"""wb""" ) )

       a_      :	int      =							os.path.join(self.tmpdirname							,		"""psgs_w100.tsv.pkl""" )
       a_      :	str      =							{sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
       pickle.dump(lowerCAmelCase_							,		open(lowerCAmelCase_							,		"""wb""" ) )

       a_      :	List[str]      =							RagConfig(
           retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		index_name="""legacy"""							,		index_path=self.tmpdirname							,		)
       a_      :	Tuple      =							RagRetriever(
           lowerCAmelCase_							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer() )
       return retriever


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							1
       a_      :	int      =							self.get_dummy_canonical_hf_index_retriever()
       a_      :	Optional[Any]      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      :	Dict      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=lowerCAmelCase_ )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertEqual(len(lowerCAmelCase_ )							,		2 )
       self.assertEqual(sorted(doc_dicts[0] )							,		["""embeddings""", """id""", """text""", """title"""] )
       self.assertEqual(len(doc_dicts[0]["""id"""] )							,		lowerCAmelCase_ )
       self.assertEqual(doc_dicts[0]["""id"""][0]							,		"""1""" )  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["""id"""][0]							,		"""0""" )  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()							,		[[1], [0]] )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_dummy_canonical_hf_index_retriever()
       with tempfile.TemporaryDirectory() as tmp_dirname:
         with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
           a_      :	str      =							self.get_dummy_dataset()
           retriever.save_pretrained(lowerCAmelCase_ )
           a_      :	Optional[Any]      =							RagRetriever.from_pretrained(lowerCAmelCase_ )
           self.assertIsInstance(lowerCAmelCase_							,		lowerCAmelCase_ )
           a_      :	List[Any]      =							np.array(
               [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
           a_      :	Optional[Any]      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=1 )
           self.assertTrue(out is not None )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							1
       a_      :	str      =							self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      :	Optional[Any]      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=lowerCAmelCase_ )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertEqual(len(lowerCAmelCase_ )							,		2 )
       self.assertEqual(sorted(doc_dicts[0] )							,		["""embeddings""", """id""", """text""", """title"""] )
       self.assertEqual(len(doc_dicts[0]["""id"""] )							,		lowerCAmelCase_ )
       self.assertEqual(doc_dicts[0]["""id"""][0]							,		"""1""" )  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["""id"""][0]							,		"""0""" )  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()							,		[[1], [0]] )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
       with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCAmelCase_ )
         a_      :	List[str]      =							RagRetriever.from_pretrained(lowerCAmelCase_ )
         self.assertIsInstance(lowerCAmelCase_							,		lowerCAmelCase_ )
         a_      :	Tuple      =							np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
         a_      :	Any      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=1 )
         self.assertTrue(out is not None )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							1
       a_      :	int      =							self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
       a_      :	int      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      :	Optional[Any]      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=lowerCAmelCase_ )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertEqual(len(lowerCAmelCase_ )							,		2 )
       self.assertEqual(sorted(doc_dicts[0] )							,		["""embeddings""", """id""", """text""", """title"""] )
       self.assertEqual(len(doc_dicts[0]["""id"""] )							,		lowerCAmelCase_ )
       self.assertEqual(doc_dicts[0]["""id"""][0]							,		"""1""" )  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["""id"""][0]							,		"""0""" )  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()							,		[[1], [0]] )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
       with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCAmelCase_ )
         a_      :	Tuple      =							RagRetriever.from_pretrained(lowerCAmelCase_ )
         self.assertIsInstance(lowerCAmelCase_							,		lowerCAmelCase_ )
         a_      :	Union[str, Any]      =							np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
         a_      :	Optional[Any]      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=1 )
         self.assertTrue(out is not None )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							1
       a_      :	List[str]      =							self.get_dummy_legacy_index_retriever()
       a_      :	Union[str, Any]      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      :	Any      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=lowerCAmelCase_ )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertEqual(len(lowerCAmelCase_ )							,		2 )
       self.assertEqual(sorted(doc_dicts[0] )							,		["""text""", """title"""] )
       self.assertEqual(len(doc_dicts[0]["""text"""] )							,		lowerCAmelCase_ )
       self.assertEqual(doc_dicts[0]["""text"""][0]							,		"""bar""" )  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["""text"""][0]							,		"""foo""" )  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()							,		[[1], [0]] )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.get_dummy_legacy_index_retriever()
       with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(lowerCAmelCase_ )
         a_      :	Dict      =							RagRetriever.from_pretrained(lowerCAmelCase_ )
         self.assertIsInstance(lowerCAmelCase_							,		lowerCAmelCase_ )
         a_      :	int      =							np.array(
             [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
         a_      :	Union[str, Any]      =							retriever.retrieve(lowerCAmelCase_							,		n_docs=1 )
         self.assertTrue(out is not None )


     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       import torch

       a_      :	List[Any]      =							1
       a_      :	Dict      =							self.get_dummy_canonical_hf_index_retriever()
       a_      :	Optional[Any]      =							[[5, 7], [10, 11]]
       a_      :	Tuple      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      :	Optional[int]      =							retriever(lowerCAmelCase_							,		lowerCAmelCase_							,		prefix=retriever.config.generator.prefix							,		n_docs=lowerCAmelCase_ )
       a_      :	List[Any]      =							(
           out["""context_input_ids"""],
           out["""context_attention_mask"""],
           out["""retrieved_doc_embeds"""],
       )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertIsInstance(lowerCAmelCase_							,		lowerCAmelCase_ )
       self.assertIsInstance(lowerCAmelCase_							,		lowerCAmelCase_ )
       self.assertIsInstance(lowerCAmelCase_							,		np.ndarray )

       a_      :	Dict      =							retriever(
           lowerCAmelCase_							,		lowerCAmelCase_							,		prefix=retriever.config.generator.prefix							,		n_docs=lowerCAmelCase_							,		return_tensors="""pt"""							,		)
       a_      :	str      =							(  # noqa: F841
           out["""context_input_ids"""],
           out["""context_attention_mask"""],
           out["""retrieved_doc_embeds"""],
           out["""doc_ids"""],
       )
       self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size) )
       self.assertIsInstance(lowerCAmelCase_							,		torch.Tensor )
       self.assertIsInstance(lowerCAmelCase_							,		torch.Tensor )
       self.assertIsInstance(lowerCAmelCase_							,		torch.Tensor )






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_dpr_ctx_encoder_tokenizer()
       a_      :	Optional[Any]      =							1
       a_      :	str      =							self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase_ )
       retriever.set_ctx_encoder_tokenizer(lowerCAmelCase_ )

       a_      :	List[Any]      =							[[5, 7], [10, 11]]
       a_      :	Optional[Any]      =							np.array(
           [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )]							,		dtype=np.floataa )
       a_      :	int      =							retriever(lowerCAmelCase_							,		lowerCAmelCase_							,		prefix=retriever.config.generator.prefix							,		n_docs=lowerCAmelCase_ )

       self.assertEqual(
           len(lowerCAmelCase_ )							,		6 )  # check whether the retriever output consist of 6 attributes including tokenized docs
       self.assertEqual(
           all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""") )							,		lowerCAmelCase_ )  # check for doc token related keys in dictionary.




'''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()
__snake_case:  List[str]								=			logging.get_logger(__name__)


__snake_case:  Dict								=			"https://openaipublic.azureedge.net/jukebox/models/"
__snake_case:  List[str]								=			{
    "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 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







  if key.endswith(""".model.1.bias"""   ) and len(key.split("""."""   )   ) > 10:
    a_      :	Optional[Any]      =							key.replace(""".model.1.bias"""     ,		""".conv1d_1.bias"""   )
  elif key.endswith(""".model.1.weight"""   ) and len(key.split("""."""   )   ) > 10:
    a_      :	Tuple      =							key.replace(""".model.1.weight"""     ,		""".conv1d_1.weight"""   )
  elif key.endswith(""".model.3.bias"""   ) and len(key.split("""."""   )   ) > 10:
    a_      :	Union[str, Any]      =							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_      :	str      =							key.replace("""conditioner_blocks.0"""     ,		"""conditioner_blocks"""   )

  if "prime_prior" in key:
    a_      :	Any      =							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_      :	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:
    a_      :	Optional[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 	_snake_case						(      A_       :    str     ,		A_       :    int     ,		A_       :    str     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	List[str]      =							{}
  import re

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

  a_      :	int      =							re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""   )
  a_      :	List[str]      =							re.compile(
      R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""   )
  a_      :	List[Any]      =							re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""   )

  a_      :	Tuple      =							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(A_   ):
      a_      :	Any      =							re_encoder_block_conv_in.match(A_   )
      a_      :	str      =							regex_match.groups()
      a_      :	Tuple      =							int(groups[2]   ) * 2 + int(groups[3]   )
      a_      :	Optional[Any]      =							f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
      a_      :	Any      =							re_encoder_block_conv_in.sub(A_     ,		A_   )

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

    elif re_encoder_block_proj_out.fullmatch(A_   ):
      a_      :	Tuple      =							re_encoder_block_proj_out.match(A_   )
      a_      :	List[str]      =							regex_match.groups()
      a_      :	int      =							f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
      a_      :	Optional[Any]      =							re_encoder_block_proj_out.sub(A_     ,		A_   )

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

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

    elif re_decoder_block_proj_in.fullmatch(A_   ):
      a_      :	Tuple      =							re_decoder_block_proj_in.match(A_   )
      a_      :	Any      =							regex_match.groups()
      a_      :	Dict      =							f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
      a_      :	Tuple      =							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_   ):
      a_      :	int      =							re_prior_cond_conv_out.match(A_   )
      a_      :	Dict      =							regex_match.groups()
      a_      :	Optional[int]      =							int(groups[1]   ) * 2 + int(groups[2]   ) - 2
      a_      :	Any      =							f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
      a_      :	str      =							re_prior_cond_conv_out.sub(A_     ,		A_   )

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

    elif re_prior_cond_proj_in.fullmatch(A_   ):
      a_      :	List[Any]      =							re_prior_cond_proj_in.match(A_   )
      a_      :	int      =							regex_match.groups()
      a_      :	Any      =							f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
      a_      :	Union[str, Any]      =							re_prior_cond_proj_in.sub(A_     ,		A_   )

    # keep original key
    else:
      a_      :	str      =							original_key

    a_      :	Any      =							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:
      a_      :	Tuple      =							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_      :	Tuple      =							original_key
    a_      :	Union[str, Any]      =							value

  return new_dict






@torch.no_grad()
def 	_snake_case						(      A_       :    Dict=None     ,		A_       :    Optional[Any]=None   ):
  """simple docstring"""







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

  a_      :	List[Any]      =							MODEL_MAPPING[model_name.split("""/"""   )[-1]]

  a_      :	Optional[Any]      =							JukeboxConfig.from_pretrained(A_   )
  a_      :	List[Any]      =							JukeboxModel(A_   )

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

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

    a_      :	List[Any]      =							"""vqvae""" if i == 0 else f'''priors.{3 - i}'''
    a_      :	Any      =							fix_jukebox_keys(A_     ,		model.state_dict()     ,		A_     ,		A_   )
    weight_dict.append(A_   )

  a_      :	str      =							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__":
    __snake_case:  Any								=			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.",
    )
    __snake_case:  Any								=			parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


