



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import numpy
     import tensorflow as tf

     from transformers import (
         TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
         BertConfig,
         DPRConfig,
         TFDPRContextEncoder,
         TFDPRQuestionEncoder,
         TFDPRReader,
     )






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__=13,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=99,		lowerCamelCase__=32,		lowerCamelCase__=2,		lowerCamelCase__=4,		lowerCamelCase__=37,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=16,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=3,		lowerCamelCase__=4,		lowerCamelCase__=None,		lowerCamelCase__=0,		):
        A			:	Dict										=       parent
        A			:	Union[str, Any]										=       batch_size
        A			:	Optional[int]										=       seq_length
        A			:	Tuple										=       is_training
        A			:	List[Any]										=       use_input_mask
        A			:	Tuple										=       use_token_type_ids
        A			:	Optional[Any]										=       use_labels
        A			:	Optional[int]										=       vocab_size
        A			:	Any										=       hidden_size
        A			:	List[str]										=       num_hidden_layers
        A			:	Any										=       num_attention_heads
        A			:	str										=       intermediate_size
        A			:	Tuple										=       hidden_act
        A			:	Optional[Any]										=       hidden_dropout_prob
        A			:	Dict										=       attention_probs_dropout_prob
        A			:	Dict										=       max_position_embeddings
        A			:	int										=       type_vocab_size
        A			:	Union[str, Any]										=       type_sequence_label_size
        A			:	Tuple										=       initializer_range
        A			:	List[Any]										=       num_labels
        A			:	Dict										=       num_choices
        A			:	Union[str, Any]										=       scope
        A			:	List[Any]										=       projection_dim






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       ids_tensor([self.batch_size, self.seq_length],		self.vocab_size		)

        A			:	Dict										=       None
        if self.use_input_mask:
            # follow test_modeling_tf_ctrl.py
            A			:	Optional[int]										=       random_attention_mask([self.batch_size, self.seq_length]		)

        A			:	int										=       None
        if self.use_token_type_ids:
            A			:	List[str]										=       ids_tensor([self.batch_size, self.seq_length],		self.type_vocab_size		)

        A			:	Tuple										=       None
        A			:	Tuple										=       None
        A			:	Optional[Any]										=       None
        if self.use_labels:
            A			:	List[Any]										=       ids_tensor([self.batch_size],		self.type_sequence_label_size		)
            A			:	List[Any]										=       ids_tensor([self.batch_size, self.seq_length],		self.num_labels		)
            A			:	Dict										=       ids_tensor([self.batch_size],		self.num_choices		)

        A			:	Optional[Any]										=       BertConfig(
            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,		is_decoder=lowerCamelCase__,		initializer_range=self.initializer_range,		)
        A			:	Dict										=       DPRConfig(projection_dim=self.projection_dim,		**config.to_dict()		)

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






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Dict										=       TFDPRContextEncoder(config=lowerCamelCase__		)
        A			:	Dict										=       model(lowerCamelCase__,		attention_mask=lowerCamelCase__,		token_type_ids=lowerCamelCase__		)
        A			:	str										=       model(lowerCamelCase__,		token_type_ids=lowerCamelCase__		)
        A			:	Dict										=       model(lowerCamelCase__		)
        self.parent.assertEqual(result.pooler_output.shape,		(self.batch_size, self.projection_dim or self.hidden_size)		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Dict										=       TFDPRQuestionEncoder(config=lowerCamelCase__		)
        A			:	List[Any]										=       model(lowerCamelCase__,		attention_mask=lowerCamelCase__,		token_type_ids=lowerCamelCase__		)
        A			:	List[Any]										=       model(lowerCamelCase__,		token_type_ids=lowerCamelCase__		)
        A			:	Any										=       model(lowerCamelCase__		)
        self.parent.assertEqual(result.pooler_output.shape,		(self.batch_size, self.projection_dim or self.hidden_size)		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       TFDPRReader(config=lowerCamelCase__		)
        A			:	List[Any]										=       model(lowerCamelCase__,		attention_mask=lowerCamelCase__		)

        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)		)
        self.parent.assertEqual(result.relevance_logits.shape,		(self.batch_size,)		)






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       self.prepare_config_and_inputs()
        (
            (
            A
        )					,							(
            A
        )					,							(
            A
        )					,							(
            A
        )					,							(
            A
        )					,							(
            A
        )					,							(
            A
        )					,							
        )			:	Dict										=       config_and_inputs
        A			:	int										=       {"""input_ids""": input_ids}
        return config, inputs_dict






@require_tf
class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase   ):




    '''simple docstring'''






    __lowerCamelCase      :			Optional[Any] =				(
        (
            TFDPRContextEncoder,
            TFDPRQuestionEncoder,
            TFDPRReader,
        )
        if is_tf_available()
        else ()
    )
    __lowerCamelCase      :			List[str] =				{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

    __lowerCamelCase      :			Optional[Any] =				False
    __lowerCamelCase      :			List[Any] =				False
    __lowerCamelCase      :			Optional[int] =				False
    __lowerCamelCase      :			Any =				False
    __lowerCamelCase      :			List[str] =				False






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       TFDPRModelTester(self		)
        A			:	Optional[int]										=       ConfigTester(self,		config_class=lowerCamelCase__,		hidden_size=37		)






    def       _lowerCAmelCase (	self		):
        self.config_tester.run_common_tests()






    def       _lowerCAmelCase (	self		):
        A			:	str										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_dpr_context_encoder(*lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	int										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_dpr_question_encoder(*lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	Union[str, Any]										=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_dpr_reader(*lowerCamelCase__		)






    @slow
    def       _lowerCAmelCase (	self		):
        for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A			:	List[Any]										=       TFDPRContextEncoder.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)

        for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A			:	Optional[Any]										=       TFDPRContextEncoder.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)

        for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A			:	Optional[Any]										=       TFDPRQuestionEncoder.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)

        for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A			:	Union[str, Any]										=       TFDPRReader.from_pretrained(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)






@require_tf
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    @slow
    def       _lowerCAmelCase (	self		):
        A			:	Any										=       TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base"""		)

        A			:	str										=       tf.constant(
            [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]		)  # [CLS] hello, is my dog cute? [SEP]
        A			:	int										=       model(lowerCamelCase__		)[0]  # embedding shape = (1, 768)
        # compare the actual values for a slice.
        A			:	Dict										=       tf.constant(
            [
                [
                    0.0323_6253,
                    0.1275_3335,
                    0.1681_8509,
                    0.0027_9786,
                    0.389_6933,
                    0.2426_4945,
                    0.217_8971,
                    -0.0233_5227,
                    -0.0848_1959,
                    -0.1432_4117,
                ]
            ]		)
        self.assertTrue(numpy.allclose(output[:, :10].numpy(),		expected_slice.numpy(),		atol=1e-4		)		)







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()
SCREAMING_SNAKE_CASE_:Union[str, Any]					=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:Union[str, Any]					=						"""https://openaipublic.azureedge.net/jukebox/models/"""
SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """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      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    if key.endswith(""".model.1.bias"""		) and len(key.split("""."""		)		) > 10:
        A			:	Optional[int]										=       key.replace(""".model.1.bias"""				,					""".conv1d_1.bias"""		)
    elif key.endswith(""".model.1.weight"""		) and len(key.split("""."""		)		) > 10:
        A			:	Any										=       key.replace(""".model.1.weight"""				,					""".conv1d_1.weight"""		)
    elif key.endswith(""".model.3.bias"""		) and len(key.split("""."""		)		) > 10:
        A			:	str										=       key.replace(""".model.3.bias"""				,					""".conv1d_2.bias"""		)
    elif key.endswith(""".model.3.weight"""		) and len(key.split("""."""		)		) > 10:
        A			:	Optional[Any]										=       key.replace(""".model.3.weight"""				,					""".conv1d_2.weight"""		)

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

    if "prime_prior" in key:
        A			:	Tuple										=       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			:	List[str]										=       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[int]										=       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      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Tuple:



    """simple docstring"""






    A			:	List[str]										=       {}
    import re

    A			:	Any										=       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""		)
    A			:	str										=       re.compile(
        R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""		)
    A			:	Union[str, Any]										=       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""		)

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

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

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

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

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_lowerCAmelCase		):
            A			:	Union[str, Any]										=       re_decoder_block_conv_out.match(_lowerCAmelCase		)
            A			:	Dict										=       regex_match.groups()
            A			:	Union[str, 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(_lowerCAmelCase				,					_lowerCAmelCase		)

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

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

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

        elif re_prior_cond_proj_in.fullmatch(_lowerCAmelCase		):
            A			:	List[Any]										=       re_prior_cond_proj_in.match(_lowerCAmelCase		)
            A			:	Optional[int]										=       regex_match.groups()
            A			:	Tuple										=       f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
            A			:	Optional[int]										=       re_prior_cond_proj_in.sub(_lowerCAmelCase				,					_lowerCAmelCase		)

        # keep original key
        else:
            A			:	str										=       original_key

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

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

    return new_dict




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase=None				,					_lowerCAmelCase=None		)	->  Tuple:



    """simple docstring"""






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

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

    A			:	List[Any]										=       JukeboxConfig.from_pretrained(_lowerCAmelCase		)
    A			:	Dict										=       JukeboxModel(_lowerCAmelCase		)

    A			:	str										=       []
    A			:	Optional[int]										=       {}
    for i, dict_name in enumerate(_lowerCAmelCase		):
        A			:	str										=       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			:	Dict										=       old_dic[k]
            elif k.endswith(""".w"""		):
                A			:	Optional[Any]										=       old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                A			:	Union[str, Any]										=       old_dic[k]
            else:
                A			:	Optional[int]										=       old_dic[k]

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

    A			:	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__":
     SCREAMING_SNAKE_CASE_:List[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.""",
     )
     SCREAMING_SNAKE_CASE_:int					=						parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


