






'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A	:			Any         ='''▁'''

_A	:			str         ={'''vocab_file''': '''spiece.model'''}

_A	:			Any         ={
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}

_A	:			Union[str, Any]         ={
    '''google/pegasus-xsum''': 512,
}


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

class _lowercase	(				_lowercase		):
							a				      =       VOCAB_FILES_NAMES

							a				      =       VOCAB_FILES_NAMES
							a				      =       PRETRAINED_VOCAB_FILES_MAP
							a				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a				      =       ["""input_ids""", """attention_mask"""]




							def __init__(							self:				Any			,							UpperCamelCase__:				int			,							UpperCamelCase__:				Union[str, Any]="<pad>"			,							UpperCamelCase__:				Optional[int]="</s>"			,							UpperCamelCase__:				Optional[Any]="<unk>"			,							UpperCamelCase__:				Optional[int]="<mask_2>"			,							UpperCamelCase__:				List[str]="<mask_1>"			,							UpperCamelCase__:				Optional[Any]=None			,							UpperCamelCase__:				Any=103			,							UpperCamelCase__:				Optional[Dict[str, Any]] = None			,							**UpperCamelCase__:				Optional[int]			,							):
														lowerCamelCase__					:	Dict         =       offset
														if additional_special_tokens is not None:
																					if not isinstance(UpperCamelCase__			,							UpperCamelCase__     ):
																												raise TypeError(
																												    F'''additional_special_tokens should be of type {type(UpperCamelCase__     )}, but is'''
																												    F''' {type(UpperCamelCase__     )}'''     )

																					lowerCamelCase__					:	Union[str, Any]         =       (
																					    ([mask_token_sent] + additional_special_tokens)
																					    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																					    else additional_special_tokens
																					)
																					# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																					additional_special_tokens_extended += [
																					    F'''<unk_{i}>''' for i in range(len(UpperCamelCase__     )			,							self.offset - 1     )
																					]

																					if len(set(UpperCamelCase__     )     ) != len(UpperCamelCase__     ):
																												raise ValueError(
																												    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
																												    F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''     )
																					lowerCamelCase__					:	Tuple         =       additional_special_tokens_extended
														else:
																					lowerCamelCase__					:	List[Any]         =       [mask_token_sent] if mask_token_sent is not None else []
																					additional_special_tokens += [F'''<unk_{i}>''' for i in range(2			,							self.offset     )]

														lowerCamelCase__					:	Any         =       {} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    eos_token=UpperCamelCase__			,							unk_token=UpperCamelCase__			,							mask_token=UpperCamelCase__			,							pad_token=UpperCamelCase__			,							mask_token_sent=UpperCamelCase__			,							offset=UpperCamelCase__			,							additional_special_tokens=UpperCamelCase__			,							sp_model_kwargs=self.sp_model_kwargs			,							**UpperCamelCase__			,							)
														lowerCamelCase__					:	Optional[int]         =       mask_token_sent
														lowerCamelCase__					:	Optional[Any]         =       vocab_file
														lowerCamelCase__					:	Any         =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
														self.sp_model.Load(UpperCamelCase__     )

														# add special tokens to encoder dict
														lowerCamelCase__					:	Dict[int, str]         =       {
														    0: self.pad_token,
														    1: self.eos_token,
														}

														if self.mask_token_sent is not None:
																					self.encoder.update(
																					    {
																					        2: self.mask_token_sent,
																					        3: self.mask_token,
																					    }     )

														if self.offset > 0:
																					# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																					# mask_token_sent is already added to list -> so start at 1
																					self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,							self.offset - 1     )}     )

														lowerCamelCase__					:	Dict[str, int]         =       {v: k for k, v in self.encoder.items()}




							@property
							def 		lowerCamelCase_				(							self:				Tuple     ):
														return len(self.sp_model     ) + self.offset




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Optional[Any]         =       {self.convert_ids_to_tokens(UpperCamelCase__     ): i for i in range(self.vocab_size     )}
														vocab.update(self.added_tokens_encoder     )
														return vocab




							def __getstate__(							self:				List[Any]     ):
														lowerCamelCase__					:	int         =       self.__dict__.copy()
														lowerCamelCase__					:	Tuple         =       None
														return state




							def __setstate__(							self:				Optional[Any]			,							UpperCamelCase__:				List[Any]     ):
														lowerCamelCase__					:	int         =       d

														# for backward compatibility
														if not hasattr(self			,							"""sp_model_kwargs"""     ):
																					lowerCamelCase__					:	Dict         =       {}

														lowerCamelCase__					:	Union[str, Any]         =       spm.SentencePieceProcessor(**self.sp_model_kwargs     )
														self.sp_model.Load(self.vocab_file     )




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				str     ):
														return self.sp_model.encode(UpperCamelCase__			,							out_type=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				str     ):
														if token in self.decoder:
																					return self.decoder[token]
														elif token in self.added_tokens_decoder:
																					return self.added_tokens_decoder[token]
														lowerCamelCase__					:	Tuple         =       self.sp_model.piece_to_id(UpperCamelCase__     )
														return sp_id + self.offset




							def 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				int     ):
														if index in self.encoder:
																					return self.encoder[index]
														elif index in self.added_tokens_encoder:
																					return self.added_tokens_encoder[index]
														else:
																					lowerCamelCase__					:	List[str]         =       self.sp_model.IdToPiece(index - self.offset     )
														return token




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				List[Any]     ):
														lowerCamelCase__					:	Any         =       []
														lowerCamelCase__					:	str         =       """"""
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												out_string += self.sp_model.decode(UpperCamelCase__     ) + token
																												lowerCamelCase__					:	List[Any]         =       []
																					else:
																												current_sub_tokens.append(UpperCamelCase__     )
														out_string += self.sp_model.decode(UpperCamelCase__     )
														return out_string.strip()




							def 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				Optional[int]=False     ):
														return 1




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				Optional[int]     ):
														lowerCamelCase__					:	List[str]         =       set(self.all_special_ids     )  # call it once instead of inside list comp
														all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

														return [1 if x in all_special_ids else 0 for x in seq]




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				List			,							UpperCamelCase__:				Optional[List] = None			,							UpperCamelCase__:				bool = False     ):
														if already_has_special_tokens:
																					return self._special_token_mask(UpperCamelCase__     )
														elif token_ids_a is None:
																					return self._special_token_mask(UpperCamelCase__     ) + [1]
														else:
																					return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				str=None     ):
														if token_ids_a is None:
																					return token_ids_a + [self.eos_token_id]
														# We don't expect to process pairs, but leave the pair logic for API consistency
														return token_ids_a + token_ids_a + [self.eos_token_id]




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Optional[str] = None     ):
														if not os.path.isdir(UpperCamelCase__     ):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
																					return
														lowerCamelCase__					:	str         =       os.path.join(
														    UpperCamelCase__			,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

														if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase__     ) and os.path.isfile(self.vocab_file     ):
																					copyfile(self.vocab_file			,							UpperCamelCase__     )
														elif not os.path.isfile(self.vocab_file     ):
																					with open(UpperCamelCase__			,							"""wb"""     ) as fi:
																												lowerCamelCase__					:	List[str]         =       self.sp_model.serialized_model_proto()
																												fi.write(UpperCamelCase__     )

														return (out_vocab_file,)









'''simple docstring'''







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()
_A	:			List[Any]         =logging.get_logger(__name__)


_A	:			Dict         =['''model.decoder.embed_positions.weights''']





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       str:
							if "emb" in name:
														lowerCamelCase__					:	Dict         =       name.replace("""emb"""				,			"""model.decoder.embed_tokens"""      )
							if "transformer" in name:
														lowerCamelCase__					:	List[str]         =       name.replace("""transformer"""				,			"""model.decoder"""      )
							if "cross_attention" in name:
														lowerCamelCase__					:	List[str]         =       name.replace("""cross_attention"""				,			"""encoder_attn"""      )
							if "linear1" in name:
														lowerCamelCase__					:	Optional[int]         =       name.replace("""linear1"""				,			"""fc1"""      )
							if "linear2" in name:
														lowerCamelCase__					:	Union[str, Any]         =       name.replace("""linear2"""				,			"""fc2"""      )
							if "norm1" in name:
														lowerCamelCase__					:	Dict         =       name.replace("""norm1"""				,			"""self_attn_layer_norm"""      )
							if "norm_cross" in name:
														lowerCamelCase__					:	Optional[Any]         =       name.replace("""norm_cross"""				,			"""encoder_attn_layer_norm"""      )
							if "norm2" in name:
														lowerCamelCase__					:	Dict         =       name.replace("""norm2"""				,			"""final_layer_norm"""      )
							if "out_norm" in name:
														lowerCamelCase__					:	Optional[Any]         =       name.replace("""out_norm"""				,			"""model.decoder.layer_norm"""      )
							if "linears" in name:
														lowerCamelCase__					:	Optional[Any]         =       name.replace("""linears"""				,			"""lm_heads"""      )
							if "condition_provider.conditioners.description.output_proj" in name:
														lowerCamelCase__					:	int         =       name.replace("""condition_provider.conditioners.description.output_proj"""				,			"""enc_to_dec_proj"""      )
							return name





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Tuple[Dict, Dict]:
							lowerCamelCase__					:	int         =       list(state_dict.keys()      )
							lowerCamelCase__					:	Tuple         =       {}
							for key in keys:
														lowerCamelCase__					:	Any         =       state_dict.pop(UpperCamelCase      )
														lowerCamelCase__					:	Union[str, Any]         =       rename_keys(UpperCamelCase      )
														if "in_proj_weight" in key:
																					# split fused qkv proj
																					lowerCamelCase__					:	Union[str, Any]         =       val[:hidden_size, :]
																					lowerCamelCase__					:	Any         =       val[hidden_size : 2 * hidden_size, :]
																					lowerCamelCase__					:	Optional[int]         =       val[-hidden_size:, :]
														elif "enc_to_dec_proj" in key:
																					lowerCamelCase__					:	str         =       val
														else:
																					lowerCamelCase__					:	Union[str, Any]         =       val
							return state_dict, enc_dec_proj_state_dict





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       MusicgenDecoderConfig:
							if checkpoint == "small":
														# default config values
														lowerCamelCase__					:	int         =       1024
														lowerCamelCase__					:	int         =       24
														lowerCamelCase__					:	List[Any]         =       16
							elif checkpoint == "medium":
														lowerCamelCase__					:	Any         =       1536
														lowerCamelCase__					:	Union[str, Any]         =       48
														lowerCamelCase__					:	Optional[int]         =       24
							elif checkpoint == "large":
														lowerCamelCase__					:	Optional[Any]         =       2048
														lowerCamelCase__					:	Dict         =       48
														lowerCamelCase__					:	List[Any]         =       32
							else:
														raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''      )
							lowerCamelCase__					:	Any         =       MusicgenDecoderConfig(
							    hidden_size=UpperCamelCase				,			ffn_dim=hidden_size * 4				,			num_hidden_layers=UpperCamelCase				,			num_attention_heads=UpperCamelCase				,			)
							return config





@torch.no_grad()
def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase=None				,			UpperCamelCase=None				,			UpperCamelCase="cpu"      )      ->       Optional[Any]:
							lowerCamelCase__					:	Optional[int]         =       MusicGen.get_pretrained(UpperCamelCase				,			device=UpperCamelCase      )
							lowerCamelCase__					:	List[Any]         =       decoder_config_from_checkpoint(UpperCamelCase      )

							lowerCamelCase__					:	Any         =       fairseq_model.lm.state_dict()
							lowerCamelCase__  ,		lowerCamelCase__					:	Optional[int]         =       rename_state_dict(
							    UpperCamelCase				,			hidden_size=decoder_config.hidden_size      )

							lowerCamelCase__					:	str         =       TaEncoderModel.from_pretrained("""t5-base"""      )
							lowerCamelCase__					:	Tuple         =       EncodecModel.from_pretrained("""facebook/encodec_32khz"""      )
							lowerCamelCase__					:	Optional[int]         =       MusicgenForCausalLM(UpperCamelCase      ).eval()

							# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
							lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       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__					:	Optional[Any]         =       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__					:	Dict         =       torch.arange(0				,			8				,			dtype=torch.long      ).reshape(2				,			-1      )
							lowerCamelCase__					:	Optional[Any]         =       input_ids.reshape(2 * 4				,			-1      )

							with torch.no_grad():
														lowerCamelCase__					:	Union[str, Any]         =       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__					:	str         =       AutoTokenizer.from_pretrained("""t5-base"""      )
							lowerCamelCase__					:	Union[str, Any]         =       AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""				,			padding_side="""left"""      )

							lowerCamelCase__					:	Optional[int]         =       MusicgenProcessor(feature_extractor=UpperCamelCase				,			tokenizer=UpperCamelCase      )

							# set the appropriate bos/pad token ids
							lowerCamelCase__					:	Union[str, Any]         =       2048
							lowerCamelCase__					:	List[str]         =       2048

							# set other default generation config params
							lowerCamelCase__					:	Optional[Any]         =       int(30 * audio_encoder.config.frame_rate      )
							lowerCamelCase__					:	Union[str, Any]         =       True
							lowerCamelCase__					:	List[Any]         =       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__":
			_A	:			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.'''
			)

			_A	:			List[str]         =parser.parse_args()
			convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)

