





import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case  :    Optional[int]						    =  logging.get_logger(__name__)


_snake_case  :    Optional[Any]						    =  ['model.decoder.embed_positions.weights']

def   a_    ( lowerCAmelCase_      :			Union[str, Any]	):
   if "emb" in name:
      __lowerCAmelCase								=	name.replace('emb',	'model.decoder.embed_tokens'	)
   if "transformer" in name:
      __lowerCAmelCase								=	name.replace('transformer',	'model.decoder'	)
   if "cross_attention" in name:
      __lowerCAmelCase								=	name.replace('cross_attention',	'encoder_attn'	)
   if "linear1" in name:
      __lowerCAmelCase								=	name.replace('linear1',	'fc1'	)
   if "linear2" in name:
      __lowerCAmelCase								=	name.replace('linear2',	'fc2'	)
   if "norm1" in name:
      __lowerCAmelCase								=	name.replace('norm1',	'self_attn_layer_norm'	)
   if "norm_cross" in name:
      __lowerCAmelCase								=	name.replace('norm_cross',	'encoder_attn_layer_norm'	)
   if "norm2" in name:
      __lowerCAmelCase								=	name.replace('norm2',	'final_layer_norm'	)
   if "out_norm" in name:
      __lowerCAmelCase								=	name.replace('out_norm',	'model.decoder.layer_norm'	)
   if "linears" in name:
      __lowerCAmelCase								=	name.replace('linears',	'lm_heads'	)
   if "condition_provider.conditioners.description.output_proj" in name:
      __lowerCAmelCase								=	name.replace('condition_provider.conditioners.description.output_proj',	'enc_to_dec_proj'	)
   return name

def   a_    ( lowerCAmelCase_      :			OrderedDict,	lowerCAmelCase_      :			int	):
   __lowerCAmelCase								=	list(state_dict.keys()	)
   __lowerCAmelCase								=	{}
   for key in keys:
      __lowerCAmelCase								=	state_dict.pop(UpperCamelCase__	)
      __lowerCAmelCase								=	rename_keys(UpperCamelCase__	)
      if "in_proj_weight" in key:
         # split fused qkv proj
         __lowerCAmelCase								=	val[:hidden_size, :]
         __lowerCAmelCase								=	val[hidden_size : 2 * hidden_size, :]
         __lowerCAmelCase								=	val[-hidden_size:, :]
      elif "enc_to_dec_proj" in key:
         __lowerCAmelCase								=	val
      else:
         __lowerCAmelCase								=	val
   return state_dict, enc_dec_proj_state_dict

def   a_    ( lowerCAmelCase_      :			str	):
   if checkpoint == "small":
      # default config values
      __lowerCAmelCase								=	1024
      __lowerCAmelCase								=	24
      __lowerCAmelCase								=	16
   elif checkpoint == "medium":
      __lowerCAmelCase								=	1536
      __lowerCAmelCase								=	48
      __lowerCAmelCase								=	24
   elif checkpoint == "large":
      __lowerCAmelCase								=	2048
      __lowerCAmelCase								=	48
      __lowerCAmelCase								=	32
   else:
      raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""	)
   __lowerCAmelCase								=	MusicgenDecoderConfig(
       hidden_size=UpperCamelCase__,	ffn_dim=hidden_size * 4,	num_hidden_layers=UpperCamelCase__,	num_attention_heads=UpperCamelCase__,	)
   return config

@torch.no_grad()
def   a_    ( lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			Union[str, Any]=None,	lowerCAmelCase_      :			List[Any]=None,	lowerCAmelCase_      :			Any="cpu"	):
   __lowerCAmelCase								=	MusicGen.get_pretrained(UpperCamelCase__,	device=UpperCamelCase__	)
   __lowerCAmelCase								=	decoder_config_from_checkpoint(UpperCamelCase__	)

   __lowerCAmelCase								=	fairseq_model.lm.state_dict()
   __lowerCAmelCase	,	__lowerCAmelCase								=	rename_state_dict(
       UpperCamelCase__,	hidden_size=decoder_config.hidden_size	)

   __lowerCAmelCase								=	TaEncoderModel.from_pretrained('t5-base'	)
   __lowerCAmelCase								=	EncodecModel.from_pretrained('facebook/encodec_32khz'	)
   __lowerCAmelCase								=	MusicgenForCausalLM(UpperCamelCase__	).eval()

   # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
   __lowerCAmelCase	,	__lowerCAmelCase								=	decoder.load_state_dict(UpperCamelCase__,	strict=UpperCamelCase__	)

   for key in missing_keys.copy():
      if key.startswith(('text_encoder', 'audio_encoder')	) or key in EXPECTED_MISSING_KEYS:
         missing_keys.remove(UpperCamelCase__	)

   if len(UpperCamelCase__	) > 0:
      raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}"""	)

   if len(UpperCamelCase__	) > 0:
      raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}"""	)

   # init the composite model
   __lowerCAmelCase								=	MusicgenForConditionalGeneration(text_encoder=UpperCamelCase__,	audio_encoder=UpperCamelCase__,	decoder=UpperCamelCase__	)

   # load the pre-trained enc-dec projection (from the decoder state dict)
   model.enc_to_dec_proj.load_state_dict(UpperCamelCase__	)

   # check we can do a forward pass
   __lowerCAmelCase								=	torch.arange(0,	8,	dtype=torch.long	).reshape(2,	-1	)
   __lowerCAmelCase								=	input_ids.reshape(2 * 4,	-1	)

   with torch.no_grad():
      __lowerCAmelCase								=	model(input_ids=UpperCamelCase__,	decoder_input_ids=UpperCamelCase__	).logits

   if logits.shape != (8, 1, 2048):
      raise ValueError('Incorrect shape for logits'	)

   # now construct the processor
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained('t5-base'	)
   __lowerCAmelCase								=	AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz',	padding_side='left'	)

   __lowerCAmelCase								=	MusicgenProcessor(feature_extractor=UpperCamelCase__,	tokenizer=UpperCamelCase__	)

   # set the appropriate bos/pad token ids
   __lowerCAmelCase								=	2048
   __lowerCAmelCase								=	2048

   # set other default generation config params
   __lowerCAmelCase								=	int(30 * audio_encoder.config.frame_rate	)
   __lowerCAmelCase								=	True
   __lowerCAmelCase								=	3.0

   if pytorch_dump_folder is not None:
      Path(UpperCamelCase__	).mkdir(exist_ok=UpperCamelCase__	)
      logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}"""	)
      model.save_pretrained(UpperCamelCase__	)
      processor.save_pretrained(UpperCamelCase__	)

   if repo_id:
      logger.info(F"""Pushing model {checkpoint} to {repo_id}"""	)
      model.push_to_hub(UpperCamelCase__	)
      processor.push_to_hub(UpperCamelCase__	)


if __name__ == "__main__":
     _snake_case  :    Dict						    =  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint',
         default='small',
         type=str,
         help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
     )
     parser.add_argument(
         '--pytorch_dump_folder',
         required=True,
         default=None,
         type=str,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument(
         '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
     )
     parser.add_argument(
         '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
     )

     _snake_case  :    Optional[int]						    =  parser.parse_args()
     convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


a__:      Dict									=		logging.get_logger(__name__)

a__:      str									=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a__:      Any									=		{
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

a__:      Any									=		{
    'distilbert-base-uncased': 512,
    'distilbert-base-uncased-distilled-squad': 512,
    'distilbert-base-cased': 512,
    'distilbert-base-cased-distilled-squad': 512,
    'distilbert-base-german-cased': 512,
    'distilbert-base-multilingual-cased': 512,
}


a__:      Optional[Any]									=		{
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	DistilBertTokenizer



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=True,__lowerCamelCase="[UNK]",__lowerCamelCase="[SEP]",__lowerCamelCase="[PAD]",__lowerCamelCase="[CLS]",__lowerCamelCase="[MASK]",__lowerCamelCase=True,__lowerCamelCase=None,**__lowerCamelCase,):
        super().__init__(
            __lowerCamelCase,tokenizer_file=__lowerCamelCase,do_lower_case=__lowerCamelCase,unk_token=__lowerCamelCase,sep_token=__lowerCamelCase,pad_token=__lowerCamelCase,cls_token=__lowerCamelCase,mask_token=__lowerCamelCase,tokenize_chinese_chars=__lowerCamelCase,strip_accents=__lowerCamelCase,**__lowerCamelCase,)

        A__											=						json.loads(self.backend_tokenizer.normalizer.__getstate__() )
        if (
            normalizer_state.get('''lowercase''',__lowerCamelCase ) != do_lower_case
            or normalizer_state.get('''strip_accents''',__lowerCamelCase ) != strip_accents
            or normalizer_state.get('''handle_chinese_chars''',__lowerCamelCase ) != tokenize_chinese_chars
        ):
               A__											=						getattr(__lowerCamelCase,normalizer_state.pop('''type''' ) )
               A__											=						do_lower_case
               A__											=						strip_accents
               A__											=						tokenize_chinese_chars
               A__											=						normalizer_class(**__lowerCamelCase )

        A__											=						do_lower_case



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=None ):
        A__											=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

        if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

        return output



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						[self.sep_token_id]
        A__											=						[self.cls_token_id]
        if token_ids_a is None:
               return len(cls + token_ids_a + sep ) * [0]
        return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						self._tokenizer.model.save(__lowerCamelCase,name=__lowerCamelCase )
        return tuple(__lowerCamelCase )





