





'''simple docstring'''


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase_   :   Dict       =					logging.get_logger(__name__)






class       UpperCamelCase__  (		A_     ):
	lowerCAmelCase__					:			Union[str, Any]								=						['''input_features''']







	def __init__(				self			:   Union[str, Any] , lowerCamelCase			:   str=8_0 , lowerCamelCase			:   Tuple=1_6_0_0_0 , lowerCamelCase			:   List[str]=1_6_0 , lowerCamelCase			:   str=3_0 , lowerCamelCase			:   Any=4_0_0 , lowerCamelCase			:   List[str]=0.0 , lowerCamelCase			:   Optional[int]=False , **lowerCamelCase			:   int , ):




				'''simple docstring'''
				super().__init__(
				    feature_size=lowerCamelCase , sampling_rate=lowerCamelCase , padding_value=lowerCamelCase , return_attention_mask=lowerCamelCase , **lowerCamelCase , )
				a__     = n_fft
				a__     = hop_length
				a__     = chunk_length
				a__     = chunk_length * sampling_rate
				a__     = self.n_samples // hop_length
				a__     = sampling_rate
				a__     = mel_filter_bank(
				    num_frequency_bins=1 + n_fft // 2 , num_mel_filters=lowerCamelCase , min_frequency=0.0 , max_frequency=8_0_0_0.0 , sampling_rate=lowerCamelCase , norm="slaney" , mel_scale="slaney" , )







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = spectrogram(
				    lowerCamelCase , window_function(self.n_fft , "hann"		) , frame_length=self.n_fft , hop_length=self.hop_length , power=2.0 , mel_filters=self.mel_filters , log_mel="log10" , )
				a__     = log_spec[:, :-1]
				a__     = np.maximum(lowerCamelCase , log_spec.max() - 8.0		)
				a__     = (log_spec + 4.0) / 4.0
				return log_spec







	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def 						__a   (				lowerCamelCase			:   List[str] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   str = 0.0		):




				'''simple docstring'''
				if attention_mask is not None:
							a__     = np.array(lowerCamelCase , np.intaa		)
							a__     = []

							for vector, length in zip(lowerCamelCase , attention_mask.sum(-1		)		):
										a__     = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7		)
										if length < normed_slice.shape[0]:
													a__     = padding_value

										normed_input_values.append(lowerCamelCase		)
				else:
							a__     = [(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

				return normed_input_values







	def __call__(				self			:   List[Any] , lowerCamelCase			:   Optional[int] , lowerCamelCase			:   Optional[Any] = True , lowerCamelCase			:   Optional[int] = None , lowerCamelCase			:   Optional[int] = None , lowerCamelCase			:   Optional[Any] = None , lowerCamelCase			:   Tuple = "max_length" , lowerCamelCase			:   Union[str, Any] = None , lowerCamelCase			:   str = None , lowerCamelCase			:   Tuple = None , **lowerCamelCase			:   str , ):




				'''simple docstring'''

				if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
										raise ValueError(
										    F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
										    F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
										    F''' was sampled with {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."		)

				a__     = 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}'''		)
				a__     = is_batched_numpy or (
				    isinstance(lowerCamelCase , (list, tuple)		) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list)		))
				)

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

				# always return batch
				if not is_batched:
							a__     = [np.asarray([raw_speech]		).T]

				a__     = BatchFeature({"input_features": raw_speech}		)

				# convert into correct format for padding

				a__     = self.pad(
				    lowerCamelCase , padding=lowerCamelCase , max_length=max_length if max_length else self.n_samples , truncation=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=return_attention_mask or do_normalize , )

				# zero-mean and unit-variance normalization
				if do_normalize:
							a__     = self.zero_mean_unit_var_norm(
							    padded_inputs["input_features"] , attention_mask=padded_inputs["attention_mask"] , padding_value=self.padding_value , )
							a__     = np.stack(padded_inputs["input_features"] , axis=0		)

				# make sure list is in array format
				a__     = padded_inputs.get("input_features"		).transpose(2 , 0 , 1		)

				a__     = [self._np_extract_fbank_features(lowerCamelCase		) for waveform in input_features[0]]

				if isinstance(input_features[0] , lowerCamelCase		):
							a__     = [np.asarray(lowerCamelCase , dtype=np.floataa		) for feature in input_features]
				else:
							a__     = input_features

				if return_attention_mask:
							# rescale from sample (48000) to feature (3000)
							a__     = padded_inputs["""attention_mask"""][:, :: self.hop_length]

				if return_tensors is not None:
							a__     = padded_inputs.convert_to_tensors(lowerCamelCase		)

				return padded_inputs







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = copy.deepcopy(self.__dict__		)
				a__     = self.__class__.__name__
				if "mel_filters" in output:
							del output["mel_filters"]
				return output










'''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_   :   List[str]       =					logging.get_logger(__name__)


lowerCAmelCase_   :   Optional[int]       =					["model.decoder.embed_positions.weights"]







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







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







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







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

			a__     = fairseq_model.lm.state_dict()
			a__   ,				a__     = rename_state_dict(
			    __lowerCamelCase					,  hidden_size=decoder_config.hidden_size     )

			a__     = TaEncoderModel.from_pretrained("t5-base"     )
			a__     = EncodecModel.from_pretrained("facebook/encodec_32khz"     )
			a__     = MusicgenForCausalLM(__lowerCamelCase     ).eval()

			# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
			a__   ,				a__     = 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
			a__     = 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
			a__     = torch.arange(0					,  8					,  dtype=torch.long     ).reshape(2					,  -1     )
			a__     = input_ids.reshape(2 * 4					,  -1     )

			with torch.no_grad():
						a__     = model(input_ids=__lowerCamelCase					,  decoder_input_ids=__lowerCamelCase     ).logits

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

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

			a__     = MusicgenProcessor(feature_extractor=__lowerCamelCase					,  tokenizer=__lowerCamelCase     )

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

			# set other default generation config params
			a__     = int(30 * audio_encoder.config.frame_rate     )
			a__     = True
			a__     = 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_   :   List[Any]       =					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_   :   str       =					parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



