from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCAmelCase__						:						Union[str, Any]		  =			logging.get_logger(__name__)
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["input_features", "attention_mask"]







					def __init__(       self     :			Optional[int]					,lowerCamelCase__     :			int=80					,lowerCamelCase__     :			Any=16_000					,lowerCamelCase__     :			int=80					,lowerCamelCase__     :			str=0.0					,lowerCamelCase__     :			Union[str, Any]=True					,lowerCamelCase__     :			str=True					,lowerCamelCase__     :			Optional[Any]=True					,**lowerCamelCase__     :			str					,):
									super().__init__(feature_size=lowerCamelCase__					,sampling_rate=lowerCamelCase__					,padding_value=lowerCamelCase__					,**lowerCamelCase__    )
									UpperCAmelCase__    =							num_mel_bins
									UpperCAmelCase__    =							do_ceptral_normalize
									UpperCAmelCase__    =							normalize_means
									UpperCAmelCase__    =							normalize_vars
									UpperCAmelCase__    =							True







					def 	__lowerCAmelCase		(       self     :			List[Any]					,lowerCamelCase__     :			np.ndarray					,):
									UpperCAmelCase__    =							waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
									UpperCAmelCase__    =							torch.from_numpy(lowerCamelCase__    ).unsqueeze(0    )
									UpperCAmelCase__    =							ta_kaldi.fbank(lowerCamelCase__					,num_mel_bins=self.num_mel_bins					,sample_frequency=self.sampling_rate    )
									return features.numpy()







					@staticmethod
					def 	__lowerCAmelCase		(       lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[bool] = True					,lowerCamelCase__     :			Optional[bool] = True					,lowerCamelCase__     :			float = 0.0					,):
									# make sure we normalize float32 arrays
									if normalize_means:
													UpperCAmelCase__    =							x[:input_length].mean(axis=0    )
													UpperCAmelCase__    =							np.subtract(lowerCamelCase__					,lowerCamelCase__    )
									if normalize_vars:
													UpperCAmelCase__    =							x[:input_length].std(axis=0    )
													UpperCAmelCase__    =							np.divide(lowerCamelCase__					,lowerCamelCase__    )

									if input_length < x.shape[0]:
													UpperCAmelCase__    =							padding_value

									# make sure array is in float32
									UpperCAmelCase__    =							x.astype(np.floataa    )

									return x







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			List[np.ndarray]					,lowerCamelCase__     :			Optional[np.ndarray] = None    ):
									UpperCAmelCase__    =							attention_mask.sum(-1    ) if attention_mask is not None else [x.shape[0] for x in input_features]
									return [
									    self.utterance_cmvn(lowerCamelCase__					,lowerCamelCase__					,self.normalize_means					,self.normalize_vars					,self.padding_value    )
									    for x, n in zip(lowerCamelCase__					,lowerCamelCase__    )
									]







					def __call__(       self     :			Dict					,lowerCamelCase__     :			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,lowerCamelCase__     :			Union[bool, str, PaddingStrategy] = False					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			Optional[Union[str, TensorType]] = None					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			Optional[bool] = None					,**lowerCamelCase__     :			Union[str, Any]					,):

									if sampling_rate is not None:
													if sampling_rate != self.sampling_rate:
																	raise ValueError(
																	    f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																	    f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
																	    f''' {self.sampling_rate} and not {sampling_rate}.'''    )
									else:
													logger.warning(
													    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
													    'Failing to do so can result in silent errors that might be hard to debug.'    )

									UpperCAmelCase__    =							isinstance(lowerCamelCase__					,np.ndarray    ) and len(raw_speech.shape    ) > 1
									if is_batched_numpy and len(raw_speech.shape    ) > 2:
													raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''    )
									UpperCAmelCase__    =							is_batched_numpy or (
									    isinstance(lowerCamelCase__					,(list, tuple)    ) and (isinstance(raw_speech[0]					,(np.ndarray, tuple, list)    ))
									)

									if is_batched:
													UpperCAmelCase__    =							[np.asarray(lowerCamelCase__					,dtype=np.floataa    ) for speech in raw_speech]
									elif not is_batched and not isinstance(lowerCamelCase__					,np.ndarray    ):
													UpperCAmelCase__    =							np.asarray(lowerCamelCase__					,dtype=np.floataa    )
									elif isinstance(lowerCamelCase__					,np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
													UpperCAmelCase__    =							raw_speech.astype(np.floataa    )

									# always return batch
									if not is_batched:
													UpperCAmelCase__    =							[raw_speech]

									# extract fbank features
									UpperCAmelCase__    =							[self._extract_fbank_features(lowerCamelCase__    ) for waveform in raw_speech]

									# convert into correct format for padding
									UpperCAmelCase__    =							BatchFeature({'input_features': features}    )

									UpperCAmelCase__    =							self.pad(
									    lowerCamelCase__					,padding=lowerCamelCase__					,max_length=lowerCamelCase__					,truncation=lowerCamelCase__					,pad_to_multiple_of=lowerCamelCase__					,return_attention_mask=lowerCamelCase__					,**lowerCamelCase__					,)

									# make sure list is in array format
									UpperCAmelCase__    =							padded_inputs.get('input_features'    )
									if isinstance(input_features[0]					,lowerCamelCase__    ):
													UpperCAmelCase__    =							[np.asarray(lowerCamelCase__					,dtype=np.floataa    ) for feature in input_features]

									UpperCAmelCase__    =							padded_inputs.get('attention_mask'    )
									if attention_mask is not None:
													UpperCAmelCase__    =							[np.asarray(lowerCamelCase__					,dtype=np.intaa    ) for array in attention_mask]

									# Utterance-level cepstral mean and variance normalization
									if self.do_ceptral_normalize:
													UpperCAmelCase__    =							(
													    np.array(lowerCamelCase__					,dtype=np.intaa    )
													    if self._get_padding_strategies(lowerCamelCase__					,max_length=lowerCamelCase__    ) is not PaddingStrategy.DO_NOT_PAD
													    else None
													)
													UpperCAmelCase__    =							self.normalize(
													    padded_inputs['input_features']					,attention_mask=lowerCamelCase__    )

									if return_tensors is not None:
													UpperCAmelCase__    =							padded_inputs.convert_to_tensors(lowerCamelCase__    )

									return padded_inputs




"""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)



