



from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


__A       =						logging.get_logger(__name__)


__A       =						OrderedDict(
    [
        # Base model mapping
        ('''albert''', '''FlaxAlbertModel'''),
        ('''bart''', '''FlaxBartModel'''),
        ('''beit''', '''FlaxBeitModel'''),
        ('''bert''', '''FlaxBertModel'''),
        ('''big_bird''', '''FlaxBigBirdModel'''),
        ('''blenderbot''', '''FlaxBlenderbotModel'''),
        ('''blenderbot-small''', '''FlaxBlenderbotSmallModel'''),
        ('''clip''', '''FlaxCLIPModel'''),
        ('''distilbert''', '''FlaxDistilBertModel'''),
        ('''electra''', '''FlaxElectraModel'''),
        ('''gpt-sw3''', '''FlaxGPT2Model'''),
        ('''gpt2''', '''FlaxGPT2Model'''),
        ('''gpt_neo''', '''FlaxGPTNeoModel'''),
        ('''gptj''', '''FlaxGPTJModel'''),
        ('''longt5''', '''FlaxLongT5Model'''),
        ('''marian''', '''FlaxMarianModel'''),
        ('''mbart''', '''FlaxMBartModel'''),
        ('''mt5''', '''FlaxMT5Model'''),
        ('''opt''', '''FlaxOPTModel'''),
        ('''pegasus''', '''FlaxPegasusModel'''),
        ('''regnet''', '''FlaxRegNetModel'''),
        ('''resnet''', '''FlaxResNetModel'''),
        ('''roberta''', '''FlaxRobertaModel'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormModel'''),
        ('''roformer''', '''FlaxRoFormerModel'''),
        ('''t5''', '''FlaxT5Model'''),
        ('''vision-text-dual-encoder''', '''FlaxVisionTextDualEncoderModel'''),
        ('''vit''', '''FlaxViTModel'''),
        ('''wav2vec2''', '''FlaxWav2Vec2Model'''),
        ('''whisper''', '''FlaxWhisperModel'''),
        ('''xglm''', '''FlaxXGLMModel'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaModel'''),
    ]
)

__A       =						OrderedDict(
    [
        # Model for pre-training mapping
        ('''albert''', '''FlaxAlbertForPreTraining'''),
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''bert''', '''FlaxBertForPreTraining'''),
        ('''big_bird''', '''FlaxBigBirdForPreTraining'''),
        ('''electra''', '''FlaxElectraForPreTraining'''),
        ('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
        ('''roberta''', '''FlaxRobertaForMaskedLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
        ('''roformer''', '''FlaxRoFormerForMaskedLM'''),
        ('''t5''', '''FlaxT5ForConditionalGeneration'''),
        ('''wav2vec2''', '''FlaxWav2Vec2ForPreTraining'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
    ]
)

__A       =						OrderedDict(
    [
        # Model for Masked LM mapping
        ('''albert''', '''FlaxAlbertForMaskedLM'''),
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''bert''', '''FlaxBertForMaskedLM'''),
        ('''big_bird''', '''FlaxBigBirdForMaskedLM'''),
        ('''distilbert''', '''FlaxDistilBertForMaskedLM'''),
        ('''electra''', '''FlaxElectraForMaskedLM'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''roberta''', '''FlaxRobertaForMaskedLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMaskedLM'''),
        ('''roformer''', '''FlaxRoFormerForMaskedLM'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMaskedLM'''),
    ]
)

__A       =						OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('''bart''', '''FlaxBartForConditionalGeneration'''),
        ('''blenderbot''', '''FlaxBlenderbotForConditionalGeneration'''),
        ('''blenderbot-small''', '''FlaxBlenderbotSmallForConditionalGeneration'''),
        ('''encoder-decoder''', '''FlaxEncoderDecoderModel'''),
        ('''longt5''', '''FlaxLongT5ForConditionalGeneration'''),
        ('''marian''', '''FlaxMarianMTModel'''),
        ('''mbart''', '''FlaxMBartForConditionalGeneration'''),
        ('''mt5''', '''FlaxMT5ForConditionalGeneration'''),
        ('''pegasus''', '''FlaxPegasusForConditionalGeneration'''),
        ('''t5''', '''FlaxT5ForConditionalGeneration'''),
    ]
)

__A       =						OrderedDict(
    [
        # Model for Image-classsification
        ('''beit''', '''FlaxBeitForImageClassification'''),
        ('''regnet''', '''FlaxRegNetForImageClassification'''),
        ('''resnet''', '''FlaxResNetForImageClassification'''),
        ('''vit''', '''FlaxViTForImageClassification'''),
    ]
)

__A       =						OrderedDict(
    [
        ('''vision-encoder-decoder''', '''FlaxVisionEncoderDecoderModel'''),
    ]
)

__A       =						OrderedDict(
    [
        # Model for Causal LM mapping
        ('''bart''', '''FlaxBartForCausalLM'''),
        ('''bert''', '''FlaxBertForCausalLM'''),
        ('''big_bird''', '''FlaxBigBirdForCausalLM'''),
        ('''electra''', '''FlaxElectraForCausalLM'''),
        ('''gpt-sw3''', '''FlaxGPT2LMHeadModel'''),
        ('''gpt2''', '''FlaxGPT2LMHeadModel'''),
        ('''gpt_neo''', '''FlaxGPTNeoForCausalLM'''),
        ('''gptj''', '''FlaxGPTJForCausalLM'''),
        ('''opt''', '''FlaxOPTForCausalLM'''),
        ('''roberta''', '''FlaxRobertaForCausalLM'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForCausalLM'''),
        ('''xglm''', '''FlaxXGLMForCausalLM'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForCausalLM'''),
    ]
)

__A       =						OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('''albert''', '''FlaxAlbertForSequenceClassification'''),
        ('''bart''', '''FlaxBartForSequenceClassification'''),
        ('''bert''', '''FlaxBertForSequenceClassification'''),
        ('''big_bird''', '''FlaxBigBirdForSequenceClassification'''),
        ('''distilbert''', '''FlaxDistilBertForSequenceClassification'''),
        ('''electra''', '''FlaxElectraForSequenceClassification'''),
        ('''mbart''', '''FlaxMBartForSequenceClassification'''),
        ('''roberta''', '''FlaxRobertaForSequenceClassification'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForSequenceClassification'''),
        ('''roformer''', '''FlaxRoFormerForSequenceClassification'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForSequenceClassification'''),
    ]
)

__A       =						OrderedDict(
    [
        # Model for Question Answering mapping
        ('''albert''', '''FlaxAlbertForQuestionAnswering'''),
        ('''bart''', '''FlaxBartForQuestionAnswering'''),
        ('''bert''', '''FlaxBertForQuestionAnswering'''),
        ('''big_bird''', '''FlaxBigBirdForQuestionAnswering'''),
        ('''distilbert''', '''FlaxDistilBertForQuestionAnswering'''),
        ('''electra''', '''FlaxElectraForQuestionAnswering'''),
        ('''mbart''', '''FlaxMBartForQuestionAnswering'''),
        ('''roberta''', '''FlaxRobertaForQuestionAnswering'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForQuestionAnswering'''),
        ('''roformer''', '''FlaxRoFormerForQuestionAnswering'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForQuestionAnswering'''),
    ]
)

__A       =						OrderedDict(
    [
        # Model for Token Classification mapping
        ('''albert''', '''FlaxAlbertForTokenClassification'''),
        ('''bert''', '''FlaxBertForTokenClassification'''),
        ('''big_bird''', '''FlaxBigBirdForTokenClassification'''),
        ('''distilbert''', '''FlaxDistilBertForTokenClassification'''),
        ('''electra''', '''FlaxElectraForTokenClassification'''),
        ('''roberta''', '''FlaxRobertaForTokenClassification'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForTokenClassification'''),
        ('''roformer''', '''FlaxRoFormerForTokenClassification'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForTokenClassification'''),
    ]
)

__A       =						OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('''albert''', '''FlaxAlbertForMultipleChoice'''),
        ('''bert''', '''FlaxBertForMultipleChoice'''),
        ('''big_bird''', '''FlaxBigBirdForMultipleChoice'''),
        ('''distilbert''', '''FlaxDistilBertForMultipleChoice'''),
        ('''electra''', '''FlaxElectraForMultipleChoice'''),
        ('''roberta''', '''FlaxRobertaForMultipleChoice'''),
        ('''roberta-prelayernorm''', '''FlaxRobertaPreLayerNormForMultipleChoice'''),
        ('''roformer''', '''FlaxRoFormerForMultipleChoice'''),
        ('''xlm-roberta''', '''FlaxXLMRobertaForMultipleChoice'''),
    ]
)

__A       =						OrderedDict(
    [
        ('''bert''', '''FlaxBertForNextSentencePrediction'''),
    ]
)

__A       =						OrderedDict(
    [
        ('''speech-encoder-decoder''', '''FlaxSpeechEncoderDecoderModel'''),
        ('''whisper''', '''FlaxWhisperForConditionalGeneration'''),
    ]
)

__A       =						OrderedDict(
    [
        ('''whisper''', '''FlaxWhisperForAudioClassification'''),
    ]
)

__A       =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__A       =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__A       =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__A       =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__A       =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__A       =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__A       =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__A       =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__A       =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__A       =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__A       =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__A       =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__A       =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__A       =						_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: Optional[Any]		       =							FLAX_MODEL_MAPPING

__A       =						auto_class_update(FlaxAutoModel)






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: Any		       =							FLAX_MODEL_FOR_PRETRAINING_MAPPING

__A       =						auto_class_update(FlaxAutoModelForPreTraining, head_doc='''pretraining''')






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: Optional[Any]		       =							FLAX_MODEL_FOR_CAUSAL_LM_MAPPING

__A       =						auto_class_update(FlaxAutoModelForCausalLM, head_doc='''causal language modeling''')






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: List[str]		       =							FLAX_MODEL_FOR_MASKED_LM_MAPPING

__A       =						auto_class_update(FlaxAutoModelForMaskedLM, head_doc='''masked language modeling''')






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: Any		       =							FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__A       =						auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='''sequence-to-sequence language modeling''', checkpoint_for_example='''t5-base'''
)






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: str		       =							FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

__A       =						auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='''sequence classification'''
)






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: List[str]		       =							FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING

__A       =						auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='''question answering''')






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: Tuple		       =							FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__A       =						auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='''token classification'''
)






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: List[str]		       =							FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING

__A       =						auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='''multiple choice''')






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: Any		       =							FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING

__A       =						auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='''next sentence prediction'''
)






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: List[Any]		       =							FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

__A       =						auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='''image classification'''
)






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: Union[str, Any]		       =							FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING

__A       =						auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='''vision-to-text modeling''')






class 		lowercase						(			_BaseAutoModelClass):






	"""simple docstring"""


	a__				: List[Any]		       =							FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING

__A       =						auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='''sequence-to-sequence speech-to-text modeling'''
)









import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__A       =						logging.get_logger(__name__)


__A       =						'''https://openaipublic.azureedge.net/jukebox/models/'''
__A       =						{
    '''jukebox-1b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''1b_lyrics/prior_level_2.pth.tar''',
    ],
    '''jukebox-5b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''5b_lyrics/prior_level_2.pth.tar''',
    ],
}



def 	__a					(   lowerCAmelCase_       : List[Any]    )				->			Tuple:






						'''simple docstring'''



						if key.endswith(""".model.1.bias"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.1.bias"""      ,""".conv1d_1.bias"""    )
						elif key.endswith(""".model.1.weight"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.1.weight"""      ,""".conv1d_1.weight"""    )
						elif key.endswith(""".model.3.bias"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.3.bias"""      ,""".conv1d_2.bias"""    )
						elif key.endswith(""".model.3.weight"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.3.weight"""      ,""".conv1d_2.weight"""    )

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

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

						if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
												UpperCAmelCase_=       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:
												UpperCAmelCase_=       key.replace("""0.x_emb.emb"""      ,"""embed_tokens"""    )

						if "prime_state_ln" in key:
												return key.replace("""prime_state_ln"""      ,"""encoder.final_layer_norm"""    )
						if ".ln" in key:
												return key.replace(""".ln"""      ,""".layer_norm"""    )
						if "_ln" in key:
												return key.replace("""_ln"""      ,"""_layer_norm"""    )

						if "prime_state_proj" in key:
												return key.replace("""prime_state_proj"""      ,"""encoder.proj_in"""    )
						if "prime_x_out" in key:
												return key.replace("""prime_x_out"""      ,"""encoder.lm_head"""    )
						if "prior.x_out" in key:
												return key.replace("""x_out"""      ,"""fc_proj_out"""    )
						if "x_emb" in key:
												return key.replace("""x_emb"""      ,"""embed_tokens"""    )

						return key



def 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Any    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       {}
						import re

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

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

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

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

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

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

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

												elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_proj_in.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
																		UpperCAmelCase_=       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_    ):
																		UpperCAmelCase_=       re_prior_cond_conv_out.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       int(groups[1]    ) * 2 + int(groups[2]    ) - 2
																		UpperCAmelCase_=       F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
																		UpperCAmelCase_=       re_prior_cond_conv_out.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

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

												elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_prior_cond_proj_in.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																		UpperCAmelCase_=       re_prior_cond_proj_in.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

												# keep original key
												else:
																		UpperCAmelCase_=       original_key

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

												UpperCAmelCase_=       original_key
												UpperCAmelCase_=       value

						return new_dict



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : List[Any]=None      ,lowerCAmelCase_       : str=None    )				->			List[str]:






						'''simple docstring'''



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

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

						UpperCAmelCase_=       JukeboxConfig.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       JukeboxModel(lowerCAmelCase_    )

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

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

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

						UpperCAmelCase_=       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__":
	__A       =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''jukebox-5b-lyrics''',
	    type=str,
	    help='''Name of the model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default='''jukebox-5b-lyrics-converted''',
	    type=str,
	    help='''Path to the output PyTorch model directory.''',
	)
	__A       =						parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




