



'''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     :    Tuple      						=					["""model.decoder.embed_positions.weights"""]



def    __lowerCamelCase						(      _lowercase	)   ->    Optional[Any]:

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



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



def    __lowerCamelCase						(      _lowercase	)   ->    MusicgenDecoderConfig:

							if checkpoint == "small":
														# default config values
														UpperCAmelCase  :	List[Any]							  =	1_0_2_4
														UpperCAmelCase  :	List[str]							  =	2_4
														UpperCAmelCase  :	Any							  =	1_6
							elif checkpoint == "medium":
														UpperCAmelCase  :	Tuple							  =	1_5_3_6
														UpperCAmelCase  :	Dict							  =	4_8
														UpperCAmelCase  :	Tuple							  =	2_4
							elif checkpoint == "large":
														UpperCAmelCase  :	int							  =	2_0_4_8
														UpperCAmelCase  :	Optional[int]							  =	4_8
														UpperCAmelCase  :	Dict							  =	3_2
							else:
														raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''	)
							UpperCAmelCase  :	str							  =	MusicgenDecoderConfig(
							    hidden_size=lowercase_			,       ffn_dim=hidden_size * 4			,       num_hidden_layers=lowercase_			,       num_attention_heads=lowercase_			,       )
							return config



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase="cpu"	)   ->    List[str]:
							UpperCAmelCase  :	str							  =	MusicGen.get_pretrained(lowercase_			,       device=lowercase_	)
							UpperCAmelCase  :	Union[str, Any]							  =	decoder_config_from_checkpoint(lowercase_	)

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

							UpperCAmelCase  :	Tuple							  =	TaEncoderModel.from_pretrained("""t5-base"""	)
							UpperCAmelCase  :	Union[str, Any]							  =	EncodecModel.from_pretrained("""facebook/encodec_32khz"""	)
							UpperCAmelCase  :	str							  =	MusicgenForCausalLM(lowercase_	).eval()

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

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

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

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

							# init the composite model
							UpperCAmelCase  :	str							  =	MusicgenForConditionalGeneration(text_encoder=lowercase_			,       audio_encoder=lowercase_			,       decoder=lowercase_	)

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

							# check we can do a forward pass
							UpperCAmelCase  :	List[str]							  =	torch.arange(0			,       8			,       dtype=torch.long	).reshape(2			,       -1	)
							UpperCAmelCase  :	Dict							  =	input_ids.reshape(2 * 4			,       -1	)

							with torch.no_grad():
														UpperCAmelCase  :	Tuple							  =	model(input_ids=lowercase_			,       decoder_input_ids=lowercase_	).logits

							if logits.shape != (8, 1, 2_0_4_8):
														raise ValueError("""Incorrect shape for logits"""	)

							# now construct the processor
							UpperCAmelCase  :	int							  =	AutoTokenizer.from_pretrained("""t5-base"""	)
							UpperCAmelCase  :	str							  =	AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""			,       padding_side="""left"""	)

							UpperCAmelCase  :	Optional[int]							  =	MusicgenProcessor(feature_extractor=lowercase_			,       tokenizer=lowercase_	)

							# set the appropriate bos/pad token ids
							UpperCAmelCase  :	str							  =	2_0_4_8
							UpperCAmelCase  :	str							  =	2_0_4_8

							# set other default generation config params
							UpperCAmelCase  :	Optional[Any]							  =	int(3_0 * audio_encoder.config.frame_rate	)
							UpperCAmelCase  :	List[str]							  =	True
							UpperCAmelCase  :	int							  =	3.0

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

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


if __name__ == "__main__":
							a     :    str      						=					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     :    Optional[int]      						=					parser.parse_args()
							convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






'''simple docstring'''




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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_barthez import BarthezTokenizer
else:
							a     :    int      						=					None

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

a     :    Dict      						=					{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a     :    Union[str, Any]      						=					{
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
        ),
    },
}

a     :    List[Any]      						=					{
    """moussaKam/mbarthez""": 1_0_2_4,
    """moussaKam/barthez""": 1_0_2_4,
    """moussaKam/barthez-orangesum-title""": 1_0_2_4,
}

a     :    int      						=					"""▁"""

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				VOCAB_FILES_NAMES
	lowercase    =				PRETRAINED_VOCAB_FILES_MAP
	lowercase    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase    =				['input_ids', 'attention_mask']
	lowercase    =				BarthezTokenizer





	def __init__(						self	,	A=None	,	A=None	,	A="<s>"	,	A="</s>"	,	A="</s>"	,	A="<s>"	,	A="<unk>"	,	A="<pad>"	,	A="<mask>"	,	**A	,	)    ->    List[Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase  :	Tuple							  =	AddedToken(A	,	lstrip=A	,	rstrip=A					) if isinstance(A	,	A					) else mask_token

								super().__init__(
								    A	,	tokenizer_file=A	,	bos_token=A	,	eos_token=A	,	unk_token=A	,	sep_token=A	,	cls_token=A	,	pad_token=A	,	mask_token=A	,	**A	,	)

								UpperCAmelCase  :	Union[str, Any]							  =	vocab_file
								UpperCAmelCase  :	int							  =	False if not self.vocab_file else True





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase  :	Optional[int]							  =	[self.cls_token_id]
								UpperCAmelCase  :	List[str]							  =	[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 				_lowercase(						self	,	A	,	A = None					)    ->    List[int]:
								UpperCAmelCase  :	Optional[int]							  =	[self.sep_token_id]
								UpperCAmelCase  :	List[Any]							  =	[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 + sep + token_ids_a + sep					) * [0]






	def 				_lowercase(						self	,	A	,	A = None					)    ->    Tuple[str]:
								if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""					)

								if not os.path.isdir(A					):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
															return
								UpperCAmelCase  :	str							  =	os.path.join(
								    A	,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(A					):
															copyfile(self.vocab_file	,	A					)

								return (out_vocab_file,)

