"""simple docstring"""






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

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


if is_sentencepiece_available():
						from .tokenization_pegasus import PegasusTokenizer
else:
						lowerCAmelCase__						:						str		  =			None


lowerCAmelCase__						:						str		  =			logging.get_logger(__name__)


lowerCAmelCase__						:						Any		  =			'▁'

lowerCAmelCase__						:						Optional[Any]		  =			{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__						:						int		  =			{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

lowerCAmelCase__						:						str		  =			{
    'google/pegasus-xsum': 512,
}
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				VOCAB_FILES_NAMES
					snake_case__       					=				PRETRAINED_VOCAB_FILES_MAP
					snake_case__       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					snake_case__       					=				PegasusTokenizer
					snake_case__       					=				["input_ids", "attention_mask"]







					def __init__(       self     :			Optional[Any]					,lowerCamelCase__     :			Union[str, Any]=None					,lowerCamelCase__     :			List[str]=None					,lowerCamelCase__     :			Tuple="<pad>"					,lowerCamelCase__     :			List[Any]="</s>"					,lowerCamelCase__     :			List[Any]="<unk>"					,lowerCamelCase__     :			Dict="<mask_2>"					,lowerCamelCase__     :			Any="<mask_1>"					,lowerCamelCase__     :			Union[str, Any]=None					,lowerCamelCase__     :			Optional[Any]=103					,**lowerCamelCase__     :			Dict					,):
									UpperCAmelCase__    =							offset

									if additional_special_tokens is not None:
													if not isinstance(lowerCamelCase__					,lowerCamelCase__    ):
																	raise TypeError(
																	    f'''additional_special_tokens should be of type {type(lowerCamelCase__    )}, but is'''
																	    f''' {type(lowerCamelCase__    )}'''    )

													UpperCAmelCase__    =							(
													    ([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(lowerCamelCase__    )					,self.offset - 1    )
													]

													if len(set(lowerCamelCase__    )    ) != len(lowerCamelCase__    ):
																	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}.'''    )
													UpperCAmelCase__    =							additional_special_tokens_extended
									else:
													UpperCAmelCase__    =							[mask_token_sent] if mask_token_sent is not None else []
													additional_special_tokens += [f'''<unk_{i}>''' for i in range(2					,self.offset    )]

									super().__init__(
									    lowerCamelCase__					,tokenizer_file=lowerCamelCase__					,pad_token=lowerCamelCase__					,eos_token=lowerCamelCase__					,unk_token=lowerCamelCase__					,mask_token=lowerCamelCase__					,mask_token_sent=lowerCamelCase__					,offset=lowerCamelCase__					,additional_special_tokens=lowerCamelCase__					,**lowerCamelCase__					,)
									UpperCAmelCase__    =							vocab_file
									UpperCAmelCase__    =							False if not self.vocab_file else True







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[Any]    ):
									UpperCAmelCase__    =							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

									if all_special_ids != set(range(len(self.additional_special_tokens    ) + 3    )    ):
													raise ValueError(
													    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
													    f''' {len(self.additional_special_tokens    )} additional_special_tokens, but got {all_special_ids}'''    )

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







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







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			int=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]					,lowerCamelCase__     :			str					,lowerCamelCase__     :			Optional[str] = None    ):
									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(lowerCamelCase__    ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
													return
									UpperCAmelCase__    =							os.path.join(
									    lowerCamelCase__					,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

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

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


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



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



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



def 						a_		(      lowerCamelCase ):
				if checkpoint == "small":
								# default config values
								UpperCAmelCase__    =							1_0_2_4
								UpperCAmelCase__    =							2_4
								UpperCAmelCase__    =							1_6
				elif checkpoint == "medium":
								UpperCAmelCase__    =							1_5_3_6
								UpperCAmelCase__    =							4_8
								UpperCAmelCase__    =							2_4
				elif checkpoint == "large":
								UpperCAmelCase__    =							2_0_4_8
								UpperCAmelCase__    =							4_8
								UpperCAmelCase__    =							3_2
				else:
								raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
				UpperCAmelCase__    =							MusicgenDecoderConfig(
				    hidden_size=lowerCamelCase      ,    ffn_dim=hidden_size * 4      ,    num_hidden_layers=lowerCamelCase      ,    num_attention_heads=lowerCamelCase      ,    )
				return config



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase=None      ,    lowerCamelCase=None      ,    lowerCamelCase="cpu" ):
				UpperCAmelCase__    =							MusicGen.get_pretrained(lowerCamelCase      ,    device=lowerCamelCase )
				UpperCAmelCase__    =							decoder_config_from_checkpoint(lowerCamelCase )

				UpperCAmelCase__    =							fairseq_model.lm.state_dict()
				UpperCAmelCase__   ,			UpperCAmelCase__    =							rename_state_dict(
				    lowerCamelCase      ,    hidden_size=decoder_config.hidden_size )

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

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

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

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

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

				# init the composite model
				UpperCAmelCase__    =							MusicgenForConditionalGeneration(text_encoder=lowerCamelCase      ,    audio_encoder=lowerCamelCase      ,    decoder=lowerCamelCase )

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

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

				with torch.no_grad():
								UpperCAmelCase__    =							model(input_ids=lowerCamelCase      ,    decoder_input_ids=lowerCamelCase ).logits

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

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

				UpperCAmelCase__    =							MusicgenProcessor(feature_extractor=lowerCamelCase      ,    tokenizer=lowerCamelCase )

				# set the appropriate bos/pad token ids
				UpperCAmelCase__    =							2_0_4_8
				UpperCAmelCase__    =							2_0_4_8

				# set other default generation config params
				UpperCAmelCase__    =							int(3_0 * audio_encoder.config.frame_rate )
				UpperCAmelCase__    =							True
				UpperCAmelCase__    =							3.0

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

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


if __name__ == "__main__":
						lowerCAmelCase__						:						Tuple		  =			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.'
						)

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



