

"""simple docstring"""






import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaVaForMaskedLM,
     DebertaVaForMultipleChoice,
     DebertaVaForQuestionAnswering,
     DebertaVaForSequenceClassification,
     DebertaVaForTokenClassification,
     DebertaVaModel,
 )
 from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST







class 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A    ,_A=13    ,_A=7    ,_A=True    ,_A=True    ,_A=True    ,_A=True    ,_A=99    ,_A=32    ,_A=5    ,_A=4    ,_A=37    ,_A="gelu"    ,_A=0.1    ,_A=0.1    ,_A=512    ,_A=16    ,_A=2    ,_A=0.0_2    ,_A=False    ,_A=True    ,_A="None"    ,_A=3    ,_A=4    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    parent
      _lowerCAmelCase       :   Dict  =    batch_size
      _lowerCAmelCase       :   List[str]  =    seq_length
      _lowerCAmelCase       :   List[Any]  =    is_training
      _lowerCAmelCase       :   Optional[Any]  =    use_input_mask
      _lowerCAmelCase       :   str  =    use_token_type_ids
      _lowerCAmelCase       :   str  =    use_labels
      _lowerCAmelCase       :   Dict  =    vocab_size
      _lowerCAmelCase       :   Optional[Any]  =    hidden_size
      _lowerCAmelCase       :   List[str]  =    num_hidden_layers
      _lowerCAmelCase       :   Union[str, Any]  =    num_attention_heads
      _lowerCAmelCase       :   Dict  =    intermediate_size
      _lowerCAmelCase       :   Optional[Any]  =    hidden_act
      _lowerCAmelCase       :   Any  =    hidden_dropout_prob
      _lowerCAmelCase       :   List[str]  =    attention_probs_dropout_prob
      _lowerCAmelCase       :   List[str]  =    max_position_embeddings
      _lowerCAmelCase       :   List[str]  =    type_vocab_size
      _lowerCAmelCase       :   Optional[Any]  =    type_sequence_label_size
      _lowerCAmelCase       :   List[Any]  =    initializer_range
      _lowerCAmelCase       :   Dict  =    num_labels
      _lowerCAmelCase       :   Union[str, Any]  =    num_choices
      _lowerCAmelCase       :   Optional[Any]  =    relative_attention
      _lowerCAmelCase       :   Tuple  =    position_biased_input
      _lowerCAmelCase       :   Any  =    pos_att_type
      _lowerCAmelCase       :   List[Any]  =    scope

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size      )

      _lowerCAmelCase       :   Optional[Any]  =    None
      if self.use_input_mask:
          _lowerCAmelCase       :   Tuple  =    ids_tensor([self.batch_size, self.seq_length]    ,vocab_size=2      )

      _lowerCAmelCase       :   Optional[int]  =    None
      if self.use_token_type_ids:
          _lowerCAmelCase       :   Tuple  =    ids_tensor([self.batch_size, self.seq_length]    ,self.type_vocab_size      )

      _lowerCAmelCase       :   Optional[Any]  =    None
      _lowerCAmelCase       :   Tuple  =    None
      _lowerCAmelCase       :   Tuple  =    None
      if self.use_labels:
          _lowerCAmelCase       :   Optional[int]  =    ids_tensor([self.batch_size]    ,self.type_sequence_label_size      )
          _lowerCAmelCase       :   Optional[Any]  =    ids_tensor([self.batch_size, self.seq_length]    ,self.num_labels      )
          _lowerCAmelCase       :   Union[str, Any]  =    ids_tensor([self.batch_size]    ,self.num_choices      )

      _lowerCAmelCase       :   Tuple  =    self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return DebertaVaConfig(
          vocab_size=self.vocab_size    ,hidden_size=self.hidden_size    ,num_hidden_layers=self.num_hidden_layers    ,num_attention_heads=self.num_attention_heads    ,intermediate_size=self.intermediate_size    ,hidden_act=self.hidden_act    ,hidden_dropout_prob=self.hidden_dropout_prob    ,attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,max_position_embeddings=self.max_position_embeddings    ,type_vocab_size=self.type_vocab_size    ,initializer_range=self.initializer_range    ,relative_attention=self.relative_attention    ,position_biased_input=self.position_biased_input    ,pos_att_type=self.pos_att_type    ,)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      self.parent.assertListEqual(list(result.loss.size()      )    ,[]      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    DebertaVaModel(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Optional[Any]  =    model(_A    ,attention_mask=_A    ,token_type_ids=_A      )[0]
      _lowerCAmelCase       :   Tuple  =    model(_A    ,token_type_ids=_A      )[0]
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A      )[0]

      self.parent.assertListEqual(list(sequence_output.size()      )    ,[self.batch_size, self.seq_length, self.hidden_size]      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    DebertaVaForMaskedLM(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A    ,attention_mask=_A    ,token_type_ids=_A    ,labels=_A      )

      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.vocab_size)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.num_labels
      _lowerCAmelCase       :   List[Any]  =    DebertaVaForSequenceClassification(_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   List[Any]  =    model(_A    ,attention_mask=_A    ,token_type_ids=_A    ,labels=_A      )
      self.parent.assertListEqual(list(result.logits.size()      )    ,[self.batch_size, self.num_labels]      )
      self.check_loss_output(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.num_labels
      _lowerCAmelCase       :   Dict  =    DebertaVaForTokenClassification(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A    ,attention_mask=_A    ,token_type_ids=_A    ,labels=_A      )
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.num_labels)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    DebertaVaForQuestionAnswering(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   str  =    model(
          _A    ,attention_mask=_A    ,token_type_ids=_A    ,start_positions=_A    ,end_positions=_A    ,)
      self.parent.assertEqual(result.start_logits.shape    ,(self.batch_size, self.seq_length)      )
      self.parent.assertEqual(result.end_logits.shape    ,(self.batch_size, self.seq_length)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    DebertaVaForMultipleChoice(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   List[Any]  =    input_ids.unsqueeze(1      ).expand(-1    ,self.num_choices    ,-1      ).contiguous()
      _lowerCAmelCase       :   int  =    token_type_ids.unsqueeze(1      ).expand(-1    ,self.num_choices    ,-1      ).contiguous()
      _lowerCAmelCase       :   str  =    input_mask.unsqueeze(1      ).expand(-1    ,self.num_choices    ,-1      ).contiguous()
      _lowerCAmelCase       :   Dict  =    model(
          _A    ,attention_mask=_A    ,token_type_ids=_A    ,labels=_A    ,)
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_choices)      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.prepare_config_and_inputs()
      (
          (
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					(
          _lowerCAmelCase
      ),					
      )       :   Tuple  =    config_and_inputs
      _lowerCAmelCase       :   List[str]  =    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict







@require_torch
class 	__UpperCamelCase    (    a__							,							a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		(
      (
          DebertaVaModel,
          DebertaVaForMaskedLM,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaForQuestionAnswering,
          DebertaVaForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  _UpperCAmelCase          =		(
      {
          "feature-extraction": DebertaVaModel,
          "fill-mask": DebertaVaForMaskedLM,
          "question-answering": DebertaVaForQuestionAnswering,
          "text-classification": DebertaVaForSequenceClassification,
          "token-classification": DebertaVaForTokenClassification,
          "zero-shot": DebertaVaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  _UpperCAmelCase          =		True
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    DebertaVaModelTester(self      )
      _lowerCAmelCase       :   List[str]  =    ConfigTester(self    ,config_class=_A    ,hidden_size=37      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.config_tester.run_common_tests()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_multiple_choice(*_A      )







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   Union[str, Any]  =    DebertaVaModel.from_pretrained(_A      )
          self.assertIsNotNone(_A      )







@require_torch
@require_sentencepiece
@require_tokenizers
class 	__UpperCamelCase    (    unittest.TestCase						):

  @unittest.skip(reason='Model not available yet'      )
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      pass







  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'      )

      _lowerCAmelCase       :   Any  =    torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
      _lowerCAmelCase       :   Optional[int]  =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
      with torch.no_grad():
          _lowerCAmelCase       :   Tuple  =    model(_A    ,attention_mask=_A      )[0]
      # compare the actual values for a slice.
      _lowerCAmelCase       :   Optional[Any]  =    torch.tensor(
          [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]      )
      self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,_A    ,atol=1E-4      )    ,F"""{output[:, 1:4, 1:4]}"""      )








"""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						=		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   ):

    '''simple docstring'''



    if key.endswith('.model.1.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.bias' ,   '.conv1d_1.bias'   )
    elif key.endswith('.model.1.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.weight' ,   '.conv1d_1.weight'   )
    elif key.endswith('.model.3.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   int  =    key.replace('.model.3.bias' ,   '.conv1d_2.bias'   )
    elif key.endswith('.model.3.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Tuple  =    key.replace('.model.3.weight' ,   '.conv1d_2.weight'   )

    if "conditioner_blocks.0." in key:
        _lowerCAmelCase       :   Dict  =    key.replace('conditioner_blocks.0' ,   'conditioner_blocks'   )

    if "prime_prior" in key:
        _lowerCAmelCase       :   str  =    key.replace('prime_prior' ,   'encoder'   )

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        _lowerCAmelCase       :   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:
        _lowerCAmelCase       :   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 ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    {}
    import re

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

    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Union[str, Any]  =    re.compile(
        R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

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

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

        elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   str  =    re_encoder_block_proj_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Union[str, Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
            _lowerCAmelCase       :   Any  =    re_encoder_block_proj_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

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

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

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

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

        elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[str]  =    regex_match.groups()
            _lowerCAmelCase       :   List[Any]  =    f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # keep original key
        else:
            _lowerCAmelCase       :   Optional[Any]  =    original_key

        _lowerCAmelCase       :   List[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:
            _lowerCAmelCase       :   Dict  =    model_state_dict[f"""{key_prefix}.{key}"""]
            print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""   )
            _lowerCAmelCase       :   Optional[int]  =    original_key

        _lowerCAmelCase       :   Union[str, Any]  =    original_key
        _lowerCAmelCase       :   Optional[Any]  =    value

    return new_dict

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase=None ,   _lowerCamelCase=None   ):

    '''simple docstring'''



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

    _lowerCAmelCase       :   Union[str, Any]  =    MODEL_MAPPING[model_name.split('/'   )[-1]]

    _lowerCAmelCase       :   Optional[Any]  =    JukeboxConfig.from_pretrained(_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    JukeboxModel(_lowerCamelCase   )

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

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

        _lowerCAmelCase       :   List[str]  =    'vqvae' if i == 0 else f"""priors.{3 - i}"""
        _lowerCAmelCase       :   Tuple  =    fix_jukebox_keys(_lowerCamelCase ,   model.state_dict() ,   _lowerCamelCase ,   _lowerCamelCase   )
        weight_dict.append(_lowerCamelCase   )

    _lowerCAmelCase       :   List[Any]  =    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)





